Financial data analysis method based on artificial intelligence

Abstract

The invention relates to the technical field of data analysis, and discloses a financial data analysis method based on artificial intelligence, which comprises a financial data analysis system. The financial data analysis system comprises a data acquisition and preprocessing module, a data storage and management module, a data analysis and modeling module, a model training and optimization module, a real-time analysis and decision support module, a visualization and report module and a security and closing scale module. And the data acquisition and preprocessing module is in data connection with the data analysis and modeling module through the data storage and management module. According to the financial data analysis method based on artificial intelligence, a model training and optimization module and a real-time analysis and decision support module of a financial data analysis system are arranged; the hyper-parameter optimization, model training and model evaluation of the model training and optimization module are utilized to train and optimize the model after modeling analysis of the data analysis and modeling module, and the effect of improving the analysis data accuracy of the financial data analysis method is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and specifically to a financial data analysis method based on artificial intelligence. Background Art

[0002] Financial data analysis refers to the process of collecting, processing, analyzing, mining, and visualizing various data generated in the financial field, which is usually used in aspects such as financial risk management, financial investment decision-making, financial product pricing, and financial market supervision. The financial data analysis method based on artificial intelligence is a collection of technologies that uses advanced technologies such as machine learning, deep learning, and natural language processing units to process and analyze financial data. These methods aim to extract valuable information from vast financial data, provide accurate predictions, risk assessments, and decision-making support.

[0003] According to the publication number CN116777634A, a financial data analysis system based on artificial intelligence is disclosed, including a data acquisition module for collecting financial data from a pre-configured financial data source, a data processing module for processing the financial data, a data storage module for storing the financial data, and a data analysis module.

[0004] Adopting the above technical solution, by training the multivariate time series model of the target node and the associated node with sample data and using the multivariate time series model to predict the future data of the target node, the prediction accuracy of the financial time series can be improved. However, in the above technical solution, the financial data analysis method based on artificial intelligence does not have a model training and optimization module, a real-time analysis and decision-making support module, a visualization and reporting module, and a security and compliance module, which affects the financial data analysis of artificial intelligence. As a result, the accuracy of the analysis data of this financial data analysis method is relatively low, and the algorithm security of this financial data analysis method is also reduced, thereby increasing the risk of data theft of this financial data analysis method. Summary of the Invention

[0005] The purpose of the present invention is to provide a financial data analysis method based on artificial intelligence to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A financial data analysis method based on artificial intelligence, including a financial data analysis system. The financial data analysis system includes a data collection and preprocessing module, a data storage and management module, a data analysis and modeling module, a model training and optimization module, a real-time analysis and decision support module, a visualization and reporting module, and a security and compliance module. The data collection and preprocessing module is data-connected to the data analysis and modeling module through the data storage and management module. The model training and optimization module includes hyperparameter optimization, model training, and model evaluation. Based on the hyperparameter optimization, grid search, random search, and Bayesian optimization can be used to optimize the data analysis model. Based on the model training, GPU and TPU hardware acceleration can be used to train the data analysis model during the training process. Based on the model evaluation, cross-validation, AUC-ROC curve, and F1-score evaluation metrics can be used to evaluate the data analysis model;

[0007] The real-time analysis and decision support module includes real-time data processing and decision support. Based on the real-time data processing, a stream processing framework can be used to process real-time financial data streams. Based on the decision support, real-time investment advice, risk management reports, market sentiment analysis, etc. can be provided;

[0008] The visualization and reporting module includes data visualization and report generation. Based on the data visualization, charts and dashboards can be used to display the analysis results. Based on the report generation, detailed analysis reports and summaries can be automatically generated. The security and compliance module includes data encryption, authentication, and data privacy. Based on the data encryption, the SSL / TLS protocol can be used to protect the security of data transmission. Based on the authentication, multi-factor authentication and OAuth technology can be used to protect user identities. Based on the data privacy, the collected financial data can comply with GDPR and HIPAA data privacy regulations.

[0009] Preferably, the data collection and preprocessing module includes a data collection unit and a data preprocessing unit. Based on the data collection unit and the data preprocessing unit, financial data can be collected and preprocessed.

[0010] By collecting data and preprocessing the data, it is convenient to use the financial data analysis method based on artificial intelligence for financial data analysis.

[0011] Preferably, the data collection unit includes market data, news and social media data, financial statement data, macroeconomic data, and other data. The market data includes stock prices, foreign exchange rates, and futures price data. The news and social media data includes financial news, Twitter, and forum data.

[0012] By collecting market data, news, and social media data, the scope of data collection can be increased, thereby improving the comprehensiveness of the financial data.

[0013] Preferably, the financial statement data includes the company's financial reports and audit report data, the macroeconomic data includes GDP, unemployment rate, and inflation rate data, and the other data includes transaction records and user behavior data.

[0014] Using the financial reports and audit report data of the company's financial statement data facilitates the collection of the company's financial statement data, and can also collect macroeconomic data and other data, further improving the comprehensiveness of the financial data.

[0015] Preferably, the data preprocessing unit includes data cleaning, data standardization, and data transformation. Based on data cleaning, missing values, outliers, and duplicate data can be removed from the collected data. Based on data standardization, the collected data can be converted into a unified format and range. Based on data transformation, text data can be converted into numerical vectors, and time series data can be differenced.

[0016] Through data cleaning, data standardization, and data transformation, it is convenient to process financial data, thereby improving the analysis speed and accuracy of the financial data.

[0017] Preferably, the data storage and management module includes a data warehouse and a data lake. Based on the data warehouse, relational databases or NoSQL databases can be used to store and manage data. Based on the data lake, raw data and intermediate results can be stored.

[0018] Through the data warehouse and data lake of the data storage and management module, the collected data can be stored and managed, and the data can be stored in the data warehouse and data lake.

[0019] Preferably, the data analysis and modeling module includes a machine learning model unit, a deep learning model unit, and a natural language processing unit. Based on the machine learning model unit, deep learning model unit, and natural language processing unit, financial data can be modeled and analyzed.

[0020] The machine learning model unit, deep learning model unit, and natural language processing unit of the data analysis and modeling module can be used to facilitate the modeling and analysis of financial data.

[0021] Preferably, the machine learning model unit includes regression models, classification models, and clustering models. The regression models include linear regression and support vector regression models. The classification models include logistic regression, random forest, and gradient boosting tree models. The clustering models include K-means clustering and hierarchical clustering models.

[0022] Through the regression model, classification model, and clustering model of the machine learning model unit, more models can be provided to further facilitate the modeling and analysis of financial data.

[0023] Preferably, the deep learning model unit includes a time series model, an image processing model, and an autoencoder. The time series model includes LSTM and GRU models. The image processing model includes a convolutional neural network model. Based on the autoencoder, it can be used for feature learning and dimensionality reduction.

[0024] Through the time series model, image processing model, and autoencoder of the deep learning model unit, more models are further provided, so that deep learning modeling of financial data can be carried out.

[0025] Preferably, the natural language processing unit includes sentiment analysis and named entity recognition. Based on the sentiment analysis, word embedding, LSTM, and Transformer technologies can be used. Based on the named entity recognition, CRF and BiLSTM technologies can be used.

[0026] Through the sentiment analysis and named entity recognition of the natural language processing unit, natural language processing of financial data can be carried out, thereby improving the convenience of processing the financial data.

[0027] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0028] First, by setting up the model training and optimization module and the real-time analysis and decision support module of the financial data analysis system, the present invention can use the hyperparameter optimization, model training, and model evaluation of the model training and optimization module to train and optimize the model after the data analysis and modeling module performs modeling analysis. The data analysis model is optimized by using grid search, random search, and Bayesian optimization through hyperparameter optimization. The data analysis model can be trained by using GPU and TPU hardware acceleration during the model training process. And through model evaluation, the data analysis model can be evaluated by using cross-validation, AUC-ROC curve, and F1 score evaluation metrics. By using the real-time data processing and decision support of the real-time analysis and decision support module, it is convenient to perform real-time analysis and provide support for the analysis data. The real-time financial data stream can be processed by using a stream processing framework through real-time data processing. And through decision support, real-time investment advice, risk management reports, market sentiment analysis, etc. can be provided for support, thereby reducing the impact of artificial intelligence on financial data analysis and playing a role in improving the accuracy of the analysis data of the financial data analysis method.

[0029] Second, by setting up a visualization and reporting module and a security and compliance module with a financial data analysis system, the present invention can facilitate the display and generation of analysis data through data visualization and report generation of the visualization and reporting module. Through data visualization, the analysis results can be displayed using charts and dashboards, and through report generation, detailed analysis reports and summaries can be automatically generated. By utilizing data encryption, authentication, and data privacy of the security and compliance module, the security of the financial data analysis data is improved. Through data encryption, the SSL / TLS protocol can be used to protect the security of data transmission. Through authentication, multi-factor authentication and OAuth technology can be used to protect user identities. And through data privacy, the collected financial data can comply with GDPR and HIPAA data privacy regulations, thereby reducing the risk of data theft in the financial data analysis method and further improving the accuracy of the analysis data in the financial data analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a system module diagram of the financial data analysis system of the present invention;

[0031] Figure 2 It is a system module diagram of the data acquisition unit of the present invention;

[0032] Figure 3 It is a system module diagram of the data preprocessing unit of the present invention;

[0033] Figure 4 It is a system module diagram of the machine learning model unit of the present invention;

[0034] Figure 5 It is a system module diagram of the deep learning model unit of the present invention;

[0035] Figure 6 It is a system module diagram of the natural language processing unit of the present invention;

[0036] Figure 7 It is a process tree diagram of the financial data analysis method based on artificial intelligence of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1

[0039] Please refer to Figures 1-7, including a financial data analysis system, which includes a data collection and preprocessing module, a data storage and management module, a data analysis and modeling module, a model training and optimization module, a real-time analysis and decision support module, a visualization and reporting module, and a security and compliance module. The data collection and preprocessing module is data-connected to the data analysis and modeling module through the data storage and management module. The model training and optimization module includes hyperparameter optimization, model training, and model evaluation. Based on hyperparameter optimization, grid search, random search, and Bayesian optimization can be used to optimize the data analysis model. Based on model training, GPU and TPU hardware acceleration can be used to train the data analysis model during the training process. Based on model evaluation, cross-validation, AUC-ROC curve, and F1-score evaluation metrics can be used to evaluate the data analysis model. By setting the model training and optimization module of the financial data analysis system, the hyperparameter optimization, model training, and model evaluation of the model training and optimization module can be used to train and optimize the model after modeling and analysis by the data analysis and modeling module. The data analysis model can be optimized using grid search, random search, and Bayesian optimization through hyperparameter optimization. The data analysis model can be trained using GPU and TPU hardware acceleration during the training process through model training. And the data analysis model can be evaluated using cross-validation, AUC-ROC curve, and F1-score evaluation metrics through model evaluation, which can improve the accuracy of analyzing data in the financial data analysis method.

[0040] The data collection and preprocessing module includes a data collection unit and a data preprocessing unit. Based on the data collection unit and the data preprocessing unit, financial data can be collected and preprocessed. By collecting data and preprocessing the data, it is convenient to perform financial data analysis using artificial intelligence-based financial data analysis methods.

[0041] The data collection unit includes market data, news and social media data, financial statement data, macroeconomic data, and other data. Market data includes stock prices, foreign exchange rates, and futures price data. News and social media data includes financial news, Twitter, and forum data. By collecting market data and news and social media data, the scope of data collection can be increased, thereby improving the comprehensiveness of the financial data.

[0042] Financial statement data includes a company's financial reports and audit report data. Macroeconomic data includes GDP, unemployment rate, and inflation rate data. Other data includes transaction records and user behavior data. The company's financial reports and audit report data in the financial statement data are used to facilitate the collection of the company's financial statement data, and the macroeconomic data and other data can also be collected, further improving the comprehensiveness of the financial data.

[0043] The data preprocessing unit includes data cleaning, data standardization, and data transformation. Based on data cleaning, missing values, outliers, and duplicate data can be removed from the collected data. Based on data standardization, the collected data can be converted into a unified format and range. Based on data transformation, text data can be converted into numerical vectors, and time series data can be differenced. Through data cleaning, data standardization, and data transformation, it is convenient to process financial data, thereby improving the analysis rate and accuracy of the financial data.

[0044] The specific implementation manner of this embodiment is as follows: When in use, when it is necessary to use the financial data analysis method based on artificial intelligence to collect, analyze, and model financial data, first, the data collection unit and the data preprocessing unit of the data collection and preprocessing module are used to collect and preprocess the financial data. By collecting data and preprocessing the data, it is convenient to use the financial data analysis method based on artificial intelligence for financial data analysis. By collecting market data, news, and social media data, the scope of data collection can be increased, thereby improving the comprehensiveness of the financial data. Using the financial reports and audit report data of companies with financial statement data facilitates the collection of the financial statement data of the companies, and it is also possible to collect macroeconomic data and other data, further improving the comprehensiveness of the financial data. Through data cleaning, data standardization, and data transformation, it is convenient to process financial data, thereby improving the analysis rate and accuracy of the financial data. The hyperparameter optimization, model training, and model evaluation of the model training and optimization module can be used to train and optimize the model after the data analysis and modeling module performs modeling and analysis. The data analysis model is optimized through hyperparameter optimization using grid search, random search, and Bayesian optimization. The data analysis model can be trained through model training using GPU and TPU hardware to accelerate the training process, and the data analysis model can be evaluated through model evaluation using cross-validation, AUC-ROC curve, and F1 score evaluation indicators.

[0045] Embodiment 2

[0046] Please refer to Figures 1-7, the real-time analysis and decision support module includes real-time data processing and decision support. Based on real-time data processing, it can use a stream processing framework to process real-time financial data streams. Based on decision support, it can provide real-time investment advice, risk management reports, market sentiment analysis, etc. By setting up the real-time analysis and decision support module of the financial data analysis system and utilizing the real-time data processing and decision support of this module, it is convenient to conduct real-time analysis and provide support for the analyzed data. Through real-time data processing, it can use a stream processing framework to process real-time financial data streams, and through decision support, it can provide support such as real-time investment advice, risk management reports, and market sentiment analysis, thereby reducing the impact on financial data analysis by artificial intelligence and playing a role in improving the accuracy of the analyzed data in the financial data analysis method.

[0047] The data storage and management module includes a data warehouse and a data lake. Based on the data warehouse, it can use a relational database or a NoSQL database to store and manage data. Based on the data lake, it can store raw data and intermediate results. Through the data warehouse and data lake of the data storage and management module, the collected data can be stored and managed, and the data can be stored in the data warehouse and data lake.

[0048] The specific implementation of this embodiment is as follows: When in use, when it is necessary to use this artificial intelligence-based financial data analysis method to collect, analyze, and model financial data, the data collected and processed by the data acquisition and preprocessing module is stored in the data warehouse and data lake of the data storage and management module. It is necessary to use the machine learning model unit, deep learning model unit, and natural language processing unit of the data analysis and modeling module to perform modeling analysis on the stored data. Through the data warehouse and data lake of the data storage and management module, the collected data can be stored and managed, and the data can be stored in the data warehouse and data lake. By using the real-time data processing and decision support of the real-time analysis and decision support module, it is convenient to conduct real-time analysis and provide support for the analyzed data. Through real-time data processing, it can use a stream processing framework to process real-time financial data streams, and through decision support, it can provide support such as real-time investment advice, risk management reports, and market sentiment analysis, thereby reducing the impact on financial data analysis by artificial intelligence.

[0049] Embodiment Three

[0050] Please refer to Figures 1-7, the visualization and reporting module includes data visualization and report generation. Based on data visualization, the analysis results can be displayed using charts and dashboards, and based on report generation, detailed analysis reports and summaries can be automatically generated. The security and compliance module includes data encryption, authentication, and data privacy. Based on data encryption, the security of data transmission can be protected using the SSL / TLS protocol. Based on authentication, user identities can be protected using multi-factor authentication and OAuth technology. Based on data privacy, the collected financial data can comply with GDPR and HIPAA data privacy regulations. By setting up the visualization and reporting module and the security and compliance module of the financial data analysis system, the data visualization and report generation of the visualization and reporting module can be used to conveniently display and generate reports for the analyzed data. Through data visualization, the analysis results can be displayed using charts and dashboards, and through report generation, detailed analysis reports and summaries can be automatically generated. By utilizing the data encryption, authentication, and data privacy of the security and compliance module, the data security of this financial data analysis is improved. Through data encryption, the security of data transmission can be protected using the SSL / TLS protocol. Through authentication, user identities can be protected using multi-factor authentication and OAuth technology. And through data privacy, the collected financial data can comply with GDPR and HIPAA data privacy regulations, thereby reducing the risk of data theft for this financial data analysis method and further improving the accuracy of the analyzed data in the financial data analysis method.

[0051] The data analysis and modeling module includes a machine learning model unit, a deep learning model unit, and a natural language processing unit. Based on the machine learning model unit, the deep learning model unit, and the natural language processing unit, financial data can be modeled and analyzed. The most important step in the financial data analysis method based on artificial intelligence is the data analysis and modeling module. The machine learning model unit, the deep learning model unit, and the natural language processing unit of the data analysis and modeling module can be utilized to conveniently model and analyze financial data.

[0052] The machine learning model unit includes regression models, classification models, and clustering models. The regression models include linear regression and support vector regression models. The classification models include logistic regression, random forest, and gradient boosting tree models. The clustering models include K-means clustering and hierarchical clustering models. Through the regression models, classification models, and clustering models of the machine learning model unit, more models can be provided to further facilitate the modeling and analysis of financial data.

[0053] The deep learning model unit includes a time series model, an image processing model, and an autoencoder. The time series model includes LSTM and GRU models. The image processing model includes a convolutional neural network model. Since the autoencoder can be used for feature learning and dimensionality reduction, through the time series model, image processing model, and autoencoder of the deep learning model unit, more models can be further provided, so as to enable deep learning modeling of financial data.

[0054] The natural language processing unit includes sentiment analysis and named entity recognition. Based on sentiment analysis, word embedding, LSTM, and Transformer technologies can be used. Based on named entity recognition, CRF and BiLSTM technologies can be used. Through the sentiment analysis and named entity recognition of the natural language processing unit, natural language processing of financial data can be performed, thereby improving the convenience of processing this financial data.

[0055] The specific implementation manner of this embodiment is as follows: When in use, when it is necessary to use the artificial intelligence-based financial data analysis method to collect, analyze, and model financial data, the machine learning model unit, deep learning model unit, and natural language processing unit of the data analysis and modeling module can be utilized to facilitate the modeling and analysis of financial data. Through the regression model, classification model, and clustering model of the machine learning model unit, more models can be provided to further facilitate the modeling and analysis of financial data. Through the time series model, image processing model, and autoencoder of the deep learning model unit, more models can be further provided, so as to enable deep learning modeling of financial data. Through the sentiment analysis and named entity recognition of the natural language processing unit, natural language processing of financial data can be performed, thereby improving the convenience of processing this financial data. The data visualization and report generation of the visualization and reporting module are utilized to facilitate the display of analysis data and the generation of reports. Through data visualization, the analysis results can be displayed using charts and dashboards, and through report generation, a detailed analysis report and summary can be automatically generated. By using data encryption, authentication, and data privacy of the security and compliance module, the data security of this financial data analysis is improved. Through data encryption, the SSL / TLS protocol can be used to protect data transmission security. Through authentication, multi-factor authentication and OAuth technologies can be used to protect user identities. And through data privacy, the collected financial data can comply with GDPR and HIPAA data privacy regulations, thereby reducing the risk of data theft in this financial data analysis method.

[0056] The working principle of the present invention is as follows: When in use, when it is necessary to collect, analyze, and model financial data using the artificial intelligence-based financial data analysis method, first, the data collection unit and data preprocessing unit of the data collection and preprocessing module are used to collect and preprocess financial data. By collecting data and preprocessing the data, it is convenient to use the artificial intelligence-based financial data analysis method for financial data analysis. By collecting market data, news, and social media data, the scope of data collection can be increased, thereby improving the comprehensiveness of the financial data. The financial reports and audit report data of the company using financial statement data are used to facilitate the collection of the company's financial statement data, and macroeconomic data and other data can also be collected to further improve the comprehensiveness of the financial data. Through data cleaning, data standardization, and data conversion, it is convenient to process financial data, thereby improving the analysis rate and accuracy of the financial data. The hyperparameter optimization, model training, and model evaluation of the model training and optimization module can be used to train and optimize the model after modeling and analysis by the data analysis and modeling module. The data analysis model is optimized through hyperparameter optimization using grid search, random search, and Bayesian optimization. The data analysis model can be trained through model training using GPU and TPU hardware acceleration for the training process, and the data analysis model can be evaluated through model evaluation using cross-validation, AUC-ROC curve, and F1 score evaluation indicators. When in use, when it is necessary to collect, analyze, and model financial data using the artificial intelligence-based financial data analysis method, the data collected and processed by the data collection and preprocessing module is stored in the data warehouse and data lake of the data storage and management module. It is necessary to use the machine learning model unit, deep learning model unit, and natural language processing unit of the data analysis and modeling module to perform modeling and analysis on the stored data. Through the data warehouse and data lake of the data storage and management module, the collected data can be stored and managed, and the data is stored in the data warehouse and data lake. The real-time data processing and decision support of the real-time analysis and decision support module are used to facilitate the real-time analysis of the analysis data and provide support. Through real-time data processing, a stream processing framework can be used to process real-time financial data streams, and through decision support, real-time investment advice, risk management reports, market sentiment analysis, etc. can be provided to reduce the impact on the artificial intelligence-based financial data analysis. When in use, when it is necessary to collect, analyze, and model financial data using the artificial intelligence-based financial data analysis method, the data collected and processed by the data collection and preprocessing module is stored in the data warehouse and data lake of the data storage and management module. It is necessary to use the machine learning model unit, deep learning model unit, and natural language processing unit of the data analysis and modeling module to perform modeling and analysis on the stored data. Through the data warehouse and data lake of the data storage and management module, the collected data can be stored and managed,Data is stored in the data warehouse and data lake, and the real-time data processing and decision support of the real-time analysis and decision support module are utilized to facilitate the real-time analysis of the analysis data and provide support. Through real-time data processing, a stream processing framework can be used to process real-time financial data streams, and through decision support, real-time investment advice, risk management reports, market sentiment analysis, etc. can be provided to reduce the impact of financial data analysis on artificial intelligence.

[0057] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0058] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An artificial intelligence-based financial data analysis method, including a financial data analysis system, characterized in that: The financial data analysis system includes a data collection and preprocessing module, a data storage and management module, a data analysis and modeling module, a model training and optimization module, a real-time analysis and decision support module, a visualization and reporting module, and a security and compliance module. The data collection and preprocessing module is data-connected to the data analysis and modeling module through the data storage and management module. The model training and optimization module includes hyperparameter optimization, model training, and model evaluation. Based on the hyperparameter optimization, grid search, random search, and Bayesian optimization can be used to optimize the data analysis model. Based on the model training, the GPU and TPU hardware acceleration can be used to train the data analysis model during the training process. Based on the model evaluation, cross-validation, AUC-ROC curve, and F1-score evaluation metrics can be used to evaluate the data analysis model. The real-time analysis and decision support module includes real-time data processing and decision support. Based on the real-time data processing, a stream processing framework can be used to process real-time financial data streams. Based on the decision support, real-time investment advice, risk management reports, market sentiment analysis, etc. can be provided. The visualization and reporting module includes data visualization and report generation. Based on the data visualization, charts and dashboards can be used to display the analysis results. Based on the report generation, detailed analysis reports and summaries can be automatically generated. The security and compliance module includes data encryption, authentication, and data privacy. Based on the data encryption, the SSL / TLS protocol can be used to protect the security of data transmission. Based on the authentication, multi-factor authentication and OAuth technology can be used to protect user identities. Based on the data privacy, the collected financial data can comply with GDPR and HIPAA data privacy regulations.

2. The financial data analysis method based on artificial intelligence according to claim 1, wherein: The data collection and preprocessing module includes a data collection unit and a data preprocessing unit. Based on the data collection unit and the data preprocessing unit, financial data can be collected and preprocessed.

3. The method for financial data analysis based on artificial intelligence according to claim 2, wherein: The data collection unit includes market data, news and social media data, financial statement data, macroeconomic data, and other data. The market data includes stock prices, foreign exchange rates, and futures price data. The news and social media data includes financial news, Twitter, and forum data.

4. The method for financial data analysis based on artificial intelligence according to claim 3, characterized in that: The financial statement data includes the company's financial reports and audit report data. The macroeconomic data includes GDP, unemployment rate, and inflation rate data. The other data includes transaction records and user behavior data.

5. The method for financial data analysis based on artificial intelligence according to claim 2, wherein: The data preprocessing unit includes data cleaning, data standardization, and data transformation. Based on the data cleaning, missing values, outliers, and duplicate data can be removed from the collected data. Based on the data standardization, the collected data can be converted into a unified format and range. Based on the data transformation, text data can be converted into numerical vectors, and time series data can be differenced.

6. The method for financial data analysis based on artificial intelligence according to claim 1, wherein: The data storage and management module includes a data warehouse and a data lake. Based on the data warehouse, relational databases or NoSQL databases can be used to store and manage data. Based on the data lake, raw data and intermediate results can be stored.

7. The method for financial data analysis based on artificial intelligence according to claim 1, wherein: The data analysis and modeling module includes a machine learning model unit, a deep learning model unit, and a natural language processing unit. Based on the machine learning model unit, deep learning model unit, and natural language processing unit, financial data can be modeled and analyzed.

8. The method for financial data analysis based on artificial intelligence according to claim 7, characterized in that: The machine learning model unit includes regression models, classification models, and clustering models. The regression models include linear regression and support vector regression models. The classification models include logistic regression, random forest, and gradient boosting tree models. The clustering models include K-means clustering and hierarchical clustering models.

9. The method for financial data analysis based on artificial intelligence according to claim 7, wherein: The deep learning model unit includes time series models, image processing models, and autoencoders. The time series models include LSTM and GRU models. The image processing models include convolutional neural network models. The autoencoder can be used for feature learning and dimensionality reduction.

10. The method for financial data analysis based on artificial intelligence according to claim 7, characterized in that: The natural language processing unit includes sentiment analysis and named entity recognition. Based on the sentiment analysis, word embedding, LSTM, and Transformer technologies can be used. Based on the named entity recognition, CRF and BiLSTM technologies can be used.

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