LU505415B1 - System and method for pushing digital financial businesses based on artifical intelligence - Google Patents
System and method for pushing digital financial businesses based on artifical intelligence Download PDFInfo
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Abstract
The present invention provides a system and method for pushing digital financial businesses based on artificial intelligence, and relates to the technical filed of digital financial. The system for pushing digital financial businesses based on artificial intelligence includes a central processing unit, a data acquisition module, a user behavior analysis module, a recommendation engine module, a risk warning module, and a feedback and optimization module. Risk data related to the financial business are stored and managed uniformly through the risk warning module, an appropriate investment strategy is recommended through analysis and mining of users' behavior data, and a real-time optimization is carried out according to user feedback.
Description
SYSTEM AND METHOD FOR PUSHING DIGITAL FINANCIAL BUSINESSES BASED
ON ARTIFICAL INTELLIGENCE
The present invention relates to the technical filed of digital financial, and especially relates to a system and method for pushing digital financial businesses based on artificial intelligence.
Digital finance is a new generation of financial services combined with traditional financial services through the Internet and information technology. The digital finance is classified according to the industrial structure of Analysys think tank, the digital finance includes Internet payment, mobile payment, on-line banking, financial service outsourcing, on-line loan, on-line insurance, on-line fund and other financial services, a rise of the digital finance technology can not only improve an efficiency of the financial services, but also promote service innovation and bring better and more accurate services.
The existing digital financial business push system needs to collect and process a large amount of user data. Includes sensitive data such as personal identity information and transaction records, which are prone to data privacy disclosures and security risks. Moreover, the existing digital financial business push system can not accurately locate customer needs, an excessive or an inaccurate push may lead to user fatigue and dissatisfaction.
In view of the shortcomings of the prior art, the present invention provides a system and method for pushing system digital financial businesses based on artificial intelligence, data privacy leakages and security risks are easy to solve, customer requirements cannot be accurately positioned, and an excessive or an inaccurate push may lead to user fatigue and dissatisfaction.
In order to achieve the above objects, the present invention is realized by the following technical solutions: a system for pushing digital financial businesses based on artificial intelligence, including a central processing unit, a data acquisition module, a user behavior analysis module, a recommendation engine module, a risk warning module and a feedback and optimization module; the central processing unit is a computing core in a system and is responsible for performing various data processing and computing tasks, the central processing unit will receive financial data from different data sources, and a pushing result is generated through calculation and processing of an algorithm model; meanwhile, the central processing unit is responsible for resource allocation and management within the system; and the central processing unit may also monitor an operation state of the system, detect and handle abnormal situations, and ensure a reliability and security of the system.
Preferably, the data acquisition module includes a data source selection unit, a data pre-processing unit and a data storage unit; the data source selection unit is responsible for obtaining data through financial exchanges, market data providers and news media channels, and data access channels are established; the data pre-processing unit is responsible for removing duplicate data, processing missing values, processing abnormal values from an original data obtained from a data source; and the data storage unit is responsible for storing processed data into a database or data warehouse, for managing and maintaining.
Preferably, the user behavior analysis module includes a user data acquisition unit, a user behavior mining unit and a user demand prediction unit; the user data acquisition unit is responsible for collecting and storing personal information, historical transaction records and click stream data of users; the user behavior mining unit is configured to understand behavior patterns and habits of users on a digital financial platform by analyzing and mining user's behavior data; and the user demand prediction unit is configured to predict financial products that users may be interested in, and recommend appropriate investment strategies by establishing a prediction model, real-time adjustment and pushing are carried out according to a real-time behavior of the user.
Preferably, the recommendation engine module includes a recommendation algorithm selection unit, a commodity recommendation and ranking unit and a real-time recommendation unit; the recommendation algorithm selection unit is responsible for selecting and optimizing a recommendation algorithm to improve an accuracy and an efficiency of a recommendation, a collaborative filtering, a content-based recommendation and a deep learning algorithm are used for the recommendation, and a selection and an optimization are carried out according to different scenarios and needs; the commodity recommendation and ranking unit is configured to recommend and rank financial products and services suitable for users according to their interests and needs; and the real-time recommendation unit is configured to feed back a recommendation result to the user in time, the real-time adjustment and the optimization are carried out according to user feedback.
Preferably, the risk warning module includes a risk data acquisition unit, a risk warning unit and a safety protection unit; the risk data acquisition unit is responsible for collecting risk data related to the financial businesses, and a storage and a management are unified; the risk warning unit is configured to timely discover potential risk events and give warning by monitoring risk data in real time and applying a risk model; and the safety protection unit is configured to carry out a safety protection on personal financial data, transaction records and account balances of users, so as to prevent data leakage and hacker attacks from causing data leakage.
Preferably, the feedback and optimization module includes a user feedback collection unit, a model optimization unit and a test and experiment unit; the user feedback collection unit is configured to understand user needs and satisfaction from multi-dimensional and multi-angles by collecting user feedback and evaluation information; the model optimization unit is configured to continuously update and optimize the model to improve a recommendation accuracy and a satisfaction element according to user feedback information and business indicators; and the test and experiment unit is responsible for testing and verifying the feedback and the optimization of the system, and at the same time, data safety and privacy protection are tested to verify an effect and a reliability of the feedback and the optimization.
Preferably, the following methods are included:
S1: data acquisition and analysis establishing users portraits and behavior models through a system to crawl financial related information on an Internet, the user's historical transaction data, behavioral data and other multi-source data, and at the same time, extracting key features and laws by analyzing and mining of big data;
S2: construction of the user portrait combining with machine learning algorithm through the user's personal information, interest preferences, risk tolerance and other data, accurately describing and classifying users to form user portraits, and the user portrait being used as a basis for subsequent financial product promotion;
S3: personalized pushing engine analyzing, using the machine learning algorithm and recommendation system technology by the system, users' current needs and situations in a real time according to the user portraits and behavior models to provide users with personalized financial services and product pushing;
S4: intelligence risk control and safety monitoring users' trading behaviors and risk indicators in real time through the system based on big data analysis and machine learning algorithm, performing a risk assessment and a warning of users by the intelligent risk control model, and at the same time, ensuring a safe storage and a transmission of user sensitive information; and
SS: feedback and optimization collecting users' feedback and evaluation through the system,continuously optimizing an algorithm model and a pushing strategy according to users' feedback data, and improving the user experience and service quality.
The present invention provides a system and method for pushing digital financial businesses based on artificial intelligence. It has the following beneficial effects: 1. according to the present invention, risk data related to the financial businesses through 5 the central processing unit and the risk warning module,and a storage and a management are unified; and at the same time, related to data privacy is configured to carry out a safety protection on personal financial data, transaction records and account balances of users, so as to prevent data leakage and hacker attacks from causing data leakage; and 2. according to the present invention, the user behavior analysis module, the recommendation engine module and the feedback and optimization module are cooperated each other, which is configured to understand behavior patterns and habits of users on a digital financial platform to predict the demand by analyzing and mining user's behavior data, so as to recommend appropriate investment strategies, the real-time adjustment and the optimization are carried out according to user feedback.
FIG. 1 is a block diagram of a digital financial service push system according to the present invention;
FIG. 2 is a block diagram of a data acquisition module according to the present invention;
FIG. 3 is a block diagram of a user behavior analysis module according to the present invention;
FIG. 4 is a block diagram of a recommendation engine module according to present invention;
FIG. 5 is a block diagram of a risk warning module according to present invention; and
FIG. 6 is a block diagram of a feedback and optimization module according to present invention.
The technical solutions in the examples of the present invention will be clearly and completely described by reference to the accompanying drawings in the examples of the present invention. It is obvious that the described examples are only some, rather than all examples of the present invention. Based on the examples in the present invention, all other examples obtained by ordinary skilled in the art without making creative efforts are included in the scope of protection of the present invention.
As shown in FIG. 1-FIG. 6, an example of the present invention provides a system for pushing digital financial businesses based on artificial intelligence, including a central processing unit, a data acquisition module, a user behavior analysis module, a recommendation engine module, a risk warning module and a feedback and optimization module; the central processing unit is a computing core in a system and is responsible for performing various data processing and computing tasks, the central processing unit will receive financial data from different data sources, and a pushing result 1s generated through calculation and processing of an algorithm model; meanwhile, the central processing unit is responsible for resource allocation and management within the system; and the central processing unit may also monitor an operation state of the system, detect and handle abnormal situations, and ensure a reliability and security of the system.
The data acquisition module includes a data source selection unit, a data pre-processing unit and a data storage unit; the data source selection unit is responsible for obtaining data through financial exchanges, market data providers and news media channels, and data access channels are established; the data pre-processing unit is responsible for removing duplicate data, processing missing values, processing abnormal values from an original data obtained from a data source; and the data storage unit is responsible for storing processed data into a database or data warehouse, for managing and maintaining.
The user behavior analysis module includes a user data acquisition unit, a user behavior mining unit and a user demand prediction unit; the user data acquisition unit 1s responsible for collecting and storing personal information, historical transaction records and click stream data of users; the user behavior mining unit is configured to understand behavior patterns and habits of users on a digital financial platform by analyzing and mining user's behavior data; and the user demand prediction unit is configured to predict financial products that users may be interested in, and recommend appropriate investment strategies by establishing a prediction model, real-time adjustment and pushing are carried out according to a real-time behavior of the user.
The recommendation engine module includes a recommendation algorithm selection unit, a commodity recommendation and ranking unit and a real-time recommendation unit; the recommendation algorithm selection unit is responsible for selecting and optimizing a recommendation algorithm to improve an accuracy and an efficiency of a recommendation, a collaborative filtering, a content-based recommendation and a deep learning algorithm are used for the recommendation, and a selection and an optimization are carried out according to different scenarios and needs; the commodity recommendation and ranking unit is configured to recommend and rank financial products and services suitable for users according to their interests and needs; and the real-time recommendation unit is configured to feed back a recommendation result to the user in time, the real-time adjustment and the optimization are carried out according to user feedback.
The risk warning module includes a risk data acquisition unit, a risk warning unit and a safety protection unit; the risk data acquisition unit is responsible for collecting risk data related to the financial businesses, and a storage and a management are unified; the risk warning unit is configured to timely discover potential risk events and give warning by monitoring risk data in real time and applying a risk model; and the safety protection unit is configured to carry out a safety protection on personal financial data, transaction records and account balances of users, so as to prevent data leakage and hacker attacks from causing data leakage.
The feedback and optimization module includes a user feedback collection unit, a model optimization unit and a test and experiment unit; the user feedback collection unit is configured to understand user needs and satisfaction from multi-dimensional and multi-angles by collecting user feedback and evaluation information, the model optimization unit is configured to continuously update and optimize the model to improve a recommendation accuracy and a satisfaction element according to user feedback information and business indicators; and the test and experiment unit is responsible for testing and verifying the feedback and the optimization of the system, and at the same time, data safety and privacy protection are tested to verify an effect and a reliability of the feedback and the optimization.
The following methods are included:
S1: data acquisition and analysis users portraits and behavior models are established through a system to crawl financial related information on an Internet, the user's historical transaction data, behavioral data and other multi-source data,and at the same time, key features and laws are extracted by analyzing and mining of big data;
S2: construction of the user portrait combined with machine learning algorithm through the user's personal information, interest preferences, risk tolerance and other data, accurately described and classified users to form user portraits, and the user portrait is used as a basis for subsequent financial product promotion;
S3: personalized pushing engine the system uses the machine learning algorithm and recommendation system technology, users' current needs and situations are analyzed in a real time according to the user portraits and behavior models to provide users with personalized financial services and product pushing; 7905475
S4: intelligence risk control and safety users' trading behaviors and risk indicators are monitored in real time through the system based on big data analysis and machine learning algorithm, a risk assessment and a warning of users are performed by the intelligent risk control model, and at the same time, a safe storage and a transmission of user sensitive information are ensured; and
SS: feedback and optimization users' feedback and evaluation are collected through the system, an algorithm model and a pushing strategy are continuously optimized according to users' feedback data, and the user experience and service quality are improved.
Although examples of the present invention have been shown and described, for those ordinary skilled in the art, it is to understood that a variety of changes, modifications, replacements and variants of these examples maybe made without departing from the principles and spirit of the present invention, the scope of the present invention is defined by the claims and their equivalents.
Claims (7)
1. A system for pushing digital financial businesses based on artificial intelligence, comprising a central processing unit, a data acquisition module, a user behavior analysis module, a recommendation engine module, a risk warning module and a feedback and optimization module; wherein the central processing unit is a computing core in a system and is responsible for performing various data processing and computing tasks, the central processing unit will receive financial data from different data sources, and a pushing result is generated through calculation and processing of an algorithm model, meanwhile, the central processing unit is responsible for resource allocation and management within the system; and the central processing unit may also monitor an operation state of the system, detect and handle abnormal situations, and ensure a reliability and security of the system.
2. The system for pushing digital financial businesses based on artificial intelligence according to claim 1, wherein the data acquisition module comprises a data source selection unit, a data pre-processing unit and a data storage unit; the data source selection unit is responsible for obtaining data through financial exchanges, market data providers and news media channels, and data access channels are established; the data pre-processing unit is responsible for removing duplicate data, processing missing values, processing abnormal values from an original data obtained from a data source; and the data storage unit is responsible for storing processed data into a database or data warehouse, for managing and maintaining.
3. The system for pushing digital financial businesses based on artificial intelligence according to claim 1, wherein the user behavior analysis module comprises a user data acquisition unit, a user behavior mining unit and a user demand prediction unit; the user data acquisition unit is responsible for collecting and storing personal information, historical transaction records and click stream data of users; the user behavior mining unit is configured to understand behavior patterns and habits of users on a digital financial platform by analyzing and mining user's behavior data; and the user demand prediction unit is configured to predict financial products that users may be interested in, and recommend appropriate investment strategies by establishing a prediction model, real-time adjustment and pushing are carried out according to a real-time behavior of the user.
4. The system for pushing digital financial businesses based on artificial intelligence according to claim 1, wherein the recommendation engine module comprises a recommendation algorithm selection unit, a commodity recommendation and ranking unit and a real-time recommendation unit; the recommendation algorithm selection unit is responsible for selecting and optimizing a recommendation algorithm to improve an accuracy and an efficiency of a recommendation, a collaborative filtering, a content-based recommendation and a deep learning algorithm are used for the recommendation, and a selection and an optimization are carried out according to different scenarios and needs; the commodity recommendation and ranking unit is configured to recommend and rank financial products and services suitable for users according to their interests and needs; and the real-time recommendation unit is configured to feed back a recommendation result to the user in time, the real-time adjustment and the optimization are carried out according to user feedback.
5. The system for pushing digital financial businesses based on artificial intelligence according to claim 1, wherein the risk warning module comprises a risk data acquisition unit, a risk warning unit and a safety protection unit; the risk data acquisition unit is responsible for collecting risk data related to the financial businesses, and a storage and a management are unified; the risk warning unit is configured to timely discover potential risk events and give warning by monitoring risk data in real time and applying a risk model; and the safety protection unit is configured to carry out a safety protection on personal financial data, transaction records and account balances of users, so as to prevent data leakage and hacker attacks from causing data leakage.
6. The system for pushing digital financial businesses based on artificial intelligence according to claim 1, wherein the feedback and optimization module comprises a user feedback collection unit, a model optimization unit and a test and experiment unit; the user feedback collection unit is configured to understand user needs and satisfaction from multi-dimensional and multi-angles by collecting user feedback and evaluation information; the model optimization unit is configured to continuously update and optimize the model to improve a recommendation accuracy and a satisfaction element according to user feedback information and business indicators; and the test and experiment unit is responsible for testing and verifying the feedback and the optimization of the system, and at the same time, data safety and privacy protection are tested to verify an effect and a reliability of the feedback and the optimization.
7. A method of using a system for pushing digital financial businesses based on artificial intelligence according to claim 1, comprising the following steps: S1: data acquisition and analysis establishing users portraits and behavior models through a system to crawl financial related information on an Internet, the user's historical transaction data, behavioral data and other multi-source data, and at the same time, extracting key features and laws by analyzing and mining of big data; S2: construction of the user portrait combining with machine learning algorithm through the user's personal information, interest preferences, risk tolerance and other data, accurately describing and classifying users to form user portraits, and the user portrait being used as a basis for subsequent financial product promotion; S3: personalized pushing engine analyzing, using the machine learning algorithm and recommendation system technology by the system, users' current needs and situations in a real time according to the user portraits and behavior models to provide users with personalized financial services and product pushing; S4: intelligence risk control and safety monitoring users' trading behaviors and risk indicators in real time through the system based on big data analysis and machine learning algorithm, performing a risk assessment and a warning of users by the intelligent risk control model, and at the same time, ensuring a safe storage and a transmission of user sensitive information; and SS: feedback and optimization collecting users’ feedback and evaluation through the system, continuously optimizing an algorithm model and a pushing strategy according to users' feedback data, and improving the user experience and service quality.
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| LU505415A LU505415B1 (en) | 2023-11-01 | 2023-11-01 | System and method for pushing digital financial businesses based on artifical intelligence |
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