Intelligent financial analysis report automatic generation system

Abstract

The invention relates to the technical field of financial management, and particularly discloses an intelligent financial analysis report automatic generation system, which comprises a data management module used for automatically collecting related financial data from a plurality of data sources and carrying out data cleaning and preprocessing on the collected data; the financial analysis module is used for performing multi-dimensional analysis on the cleaned financial data based on an artificial intelligence technology, identifying potential rules in the financial data and generating a financial analysis report; the RPA automation module is used for automatically generating each step of the financial analysis report by utilizing an RPA technology and realizing automatic processing of content updating, data auditing and report distribution of the financial analysis report; through efficient data processing, intelligent analysis, automatic report generation and distribution, personalized decision support and real-time risk monitoring, the financial analysis depth and automation and intelligence level are comprehensively improved, enterprises are assisted to make more accurate decisions, and the financial management process is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of financial management, and particularly relates to an intelligent financial analysis report automatic generation system. Background Art

[0003] In recent years, with the rapid development of technologies such as artificial intelligence, mobile Internet, big data, and automation, the enterprise management concept has undergone a brand-new transformation. Integrating technologies such as "big intelligence, mobile, cloud, and area" with the business and activities of enterprises can not only promote the high-quality development of enterprises, but also help build a world-class financial management system, making management more intelligent and scientific. In order to improve the efficiency and accuracy of financial analysis, automated and intelligent financial analysis methods have become an important development trend. At present, there are already some financial analysis systems on the market, but most of the systems mainly focus on single financial data processing and report generation, lacking the ability of multi-dimensional data analysis, and having poor adaptability to the actual needs of enterprises, making it difficult to achieve customized analysis. In addition, the financial report generation process of the existing technologies usually relies on manual intervention, and the update and distribution of reports also lack effective automated management, unable to cope with the increasing complex needs of enterprises. Most of the existing financial analysis systems lack intelligent decision-making recommendation capabilities and are difficult to provide personalized and accurate financial decision-making suggestions for enterprises.

[0004] Therefore, it is necessary to propose an intelligent financial analysis report automatic generation system to solve the problems of lack of depth in analysis, low degree of automation and intelligence in the existing technologies.

[0005] The above information disclosed in this background art is only used to increase the understanding of the background art of the present invention. Therefore, it may include prior arts that are not known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent financial analysis report automatic generation system to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] An intelligent financial analysis report automatic generation system, comprising:

[0009] A data management module, used for automatically collecting relevant financial data from multiple data sources and performing data cleaning and preprocessing on the collected data;

[0010] A financial analysis module, used for performing multi-dimensional analysis on the cleaned financial data based on artificial intelligence technology, identifying potential rules in the financial data, and generating a financial analysis report;

[0011] The RPA automation module is used to automate each step of generating financial analysis reports using RPA technology, and to automate the content update, data review, and report distribution of financial analysis reports;

[0012] The report customization module is used to provide options for specific financial indicators and analysis dimensions according to the characteristics of the enterprise, and to generate customized reports that meet the needs of users;

[0013] The decision recommendation module is used to provide personalized financial decision-making suggestions and trend predictions based on financial analysis reports;

[0014] The push and feedback module is used to automatically push financial analysis reports to relevant personnel according to user needs, and to optimize and adjust the content of financial analysis reports according to feedback information;

[0015] The monitoring and warning module is used to monitor financial data in real time and automatically issue risk warnings according to user-set rules.

[0016] Preferably, the data management module is also used to connect to the financial management system, ERP system, and external data sources through API interfaces and database connections to automatically collect financial data;

[0017] Adopt machine learning algorithms to automatically detect and process missing data, correct abnormal data, and integrate financial data from multiple data sources into a unified structure;

[0018] Adopt time series analysis algorithms to perform consistency detection on financial data and exclude data inconsistency problems caused by time deviations.

[0019] Preferably, the financial analysis module is also used to use the FP-growth algorithm to mine association rules from the cleaned financial data, analyze the association relationships between different financial indicators, and discover potential influencing factors;

[0020] Based on the influencing factors, adopt machine learning and data mining technologies to perform predictive analysis on financial data, calculate the risk scores of financial data, and identify potential financial risks;

[0021]

[0022] where β 0 , β 1 ,..., β n are risk coefficients, and X 1 , X 2 ,..., X n are the characteristics of financial data;

[0023] Adopt the data segmentation method to segment with the third-level headings and numbers of the financial analysis report as nodes, and construct questions and answers;

[0024] Integrate ChatGPT and utilize the constructed questions and answers in combination with the GPTs function for training, understand and summarize financial data, and extract analysis results;

[0025] Based on the analysis results, generate structured report content through ChatGPT, and automatically add data interpretation, trend prediction, and financial health analysis information;

[0026] Introduce an adaptive algorithm to dynamically adjust the analysis model according to the business needs of different enterprises, real-time identify potential risks and opportunities, and provide corresponding financial analysis reports.

[0027] Preferably, the report customization module is also used to present in a graphical form based on the financial analysis report by using visualization technology, and provide report exports in multiple formats;

[0028] Customize the content, format, time range, and specific indicators of the financial analysis report according to different user needs;

[0029] Integrate natural language processing technology, provide a function for formulating customization requirements in voice or text input mode, use the TexRank algorithm for text summarization and extraction, and automatically generate corresponding customized report content:

[0030]

[0031] In the formula, S(v j ) is the importance of the word v j , w jy is the edge weight between the words v j and v y , d is the damping factor, and N(v j ) is the neighbor of the word v j .

[0032] Preferably, the decision recommendation module is also used to compare and analyze the financial analysis report with other companies in the same industry through the DBSCAN clustering algorithm, identify the financial status of benchmark companies in the same industry, and provide improvement measures and optimization suggestions;

[0033] Combine industry standards and competitor data to automatically generate a comparison report with the industry average level, point out financial advantages and disadvantages, and provide strategic suggestions for improving performance;

[0034] Based on the financial analysis report, use the local outlier factor algorithm to detect anomalies in financial data and identify abnormal fluctuations or potential fraud behaviors in the financial data;

[0035]

[0036] In the formula, N k (q) is the k-nearest neighbor of point q, and lrd is the local reachability density;

[0037] A risk assessment report automatically generated in combination with potential financial risks, and relevant optimization measures are recommended.

[0038] Preferably, the push and feedback module is further configured to use a combination of content-based recommendation algorithm and collaborative filtering algorithm to push personalized financial analysis reports to different users;

[0039] Automatically adjust the push order according to the urgency and timeliness of the report through the priority push algorithm;

[0040] Utilize the time window and user schedule to push high-priority reports and avoid conflicts between report pushing and other work of the user;

[0041] Collect feedback information of users on the financial analysis report through user behavior tracking and feedback mechanism;

[0042] After each push, adjust the report generation strategy according to the feedback information through the reinforcement learning algorithm:

[0043]

[0044] In the formula, Q(s,a) represents the expected return of taking action a in state s, α is the learning rate, and γ is the discount factor.

[0045] Preferably, the monitoring and warning module is further configured to use the Apache Flink stream processing framework to obtain financial data from multiple data sources in real time and perform real-time analysis;

[0046] Introduce a composite rule engine to implement different types of risk warnings triggered based on user-set rules;

[0047] Continuously optimize the user-set rules through a self-learning mechanism to respond to possible financial risks in the first time;

[0048] Integrate a collaborative work platform to send risk warnings to relevant personnel in real time and adjust operation strategies or financial decisions.

[0049] Preferably, the RPA automation module is further configured to use deep learning and natural language processing technologies to automatically proofread the generated report, and check the compliance, logic and format of the report content;

[0050] Implement the distribution and viewing of financial analysis reports to be limited to specific personnel through RPA automation control permissions, and monitor the reading status of the reports;

[0051] Update the report content through a timing update or incremental update mechanism, and track and manage the updated content through a version control system.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] The present invention automatically collects and cleans financial data from multiple data sources, uses artificial intelligence technology to perform multi-dimensional analysis on the cleaned data, automatically identifies potential patterns, generates high-quality financial analysis reports, and provides accurate financial insights; users can flexibly select financial indicators and analysis dimensions according to enterprise needs to generate personalized reports, ensuring that the content highly matches the enterprise needs; at the same time, provides personalized financial decision-making suggestions and trend predictions for the enterprise through intelligent analysis, helping the enterprise make more scientific decisions.

[0054] In addition, the system realizes the automation of the financial report generation process, from content update, review to distribution, greatly improving work efficiency and reducing the risk of human intervention. Automatically push the report to relevant personnel and continuously optimize according to the feedback information to improve the practicality and accuracy of the report. Real-time monitor financial data and automatically issue early warnings when potential risks are found to help the enterprise respond to financial risks in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a framework diagram of the intelligent financial analysis report automatic generation system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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 the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] Embodiment 1:

[0058] Please refer to Figure 1 As shown, an intelligent financial analysis report automatic generation system includes:

[0059] A data management module for automatically collecting relevant financial data from multiple data sources and performing data cleaning and preprocessing on the collected data;

[0060] A financial analysis module for performing multi-dimensional analysis on the cleaned financial data based on artificial intelligence technology, identifying potential patterns in the financial data, and generating a financial analysis report;

[0061] The RPA automation module is used to automate each step of generating financial analysis reports using RPA technology, and to automate the content update, data review, and report distribution of financial analysis reports;

[0062] The report customization module is used to provide options for specific financial indicators and analysis dimensions according to the characteristics of the enterprise, and to generate customized reports that meet the needs of users;

[0063] The decision recommendation module is used to provide personalized financial decision-making suggestions and trend predictions based on financial analysis reports;

[0064] The push and feedback module is used to automatically push financial analysis reports to relevant personnel according to user needs, and to optimize and adjust the content of financial analysis reports according to feedback information;

[0065] The monitoring and early warning module is used to monitor financial data in real time and automatically issue risk warnings according to user-set rules.

[0066] The data management module is also used to connect to the financial management system, ERP system, and external data sources through API interfaces and database connections to automatically collect financial data;

[0067] Adopt machine learning algorithms to automatically detect and process missing data, correct abnormal data, and integrate financial data from multiple data sources into a unified structure;

[0068] Adopt time series analysis algorithms to perform consistency detection on financial data and eliminate data inconsistency problems caused by time deviations.

[0069] Furthermore, the data management module automatically collects financial data from multiple data sources, and uses machine learning algorithms to clean and preprocess the data, and integrates it into a unified structure. This improves the efficiency of data collection and processing, reduces the need for manual intervention, and ensures the integrity and consistency of the data.

[0070] The financial analysis module is also used to use the FP-growth algorithm to mine association rules from the cleaned financial data, analyze the association relationships between different financial indicators, and discover potential influencing factors;

[0071] Based on the influencing factors, use machine learning and data mining technologies to perform predictive analysis on financial data, calculate the risk scores of financial data, and identify potential financial risks;

[0072] Adopt a data segmentation method to segment using the third-level headings and numbers of financial analysis reports as nodes, and construct questions and answers;

[0073] Integrate ChatGPT and utilize the constructed questions and answers combined with GPTs functions to train, understand, and summarize financial data, and extract analysis results;

[0074] Based on the analysis results, generate structured report content through ChatGPT and automatically add data explanations, trend predictions, and financial health analysis information;

[0075] Introduce an adaptive algorithm to dynamically adjust the analysis model according to the business needs of different enterprises, real-time identify potential risks and opportunities, and provide corresponding financial analysis reports.

[0076] Furthermore, the financial analysis module, based on artificial intelligence technology, uses the FP-growth algorithm and machine learning algorithms to conduct multi-dimensional analysis and prediction of financial data, identify potential financial risks, and calculate financial risk scores, providing a more accurate basis for financial decision-making. Through these intelligent analyses, the system can extract and discover potential problems to help enterprises respond to risks in a timely manner.

[0077] The report customization module is also used to present the financial analysis report in a graphical form based on visualization technology and provide report exports in multiple formats;

[0078] Customize the content, format, time range, and specific indicators of the financial analysis report according to different user needs;

[0079] Integrate natural language processing technology to provide a function for formulating customized requirements in voice or text input mode, use the TexRank algorithm for text summarization and extraction, and automatically generate corresponding customized report content.

[0080] Furthermore, the report customization module can customize the content, format, time range, and specific indicators of the financial analysis report according to the characteristics of the enterprise and user needs, making the report more in line with the actual needs of users. Combining visualization technology and natural language processing technology enhances the readability of the report and the user's customization experience.

[0081] The decision recommendation module is also used to conduct a comparative analysis of the financial analysis report with other companies in the same industry through the DBSCAN clustering algorithm, identify the financial status of benchmark companies in the same industry, and provide improvement measures and optimization suggestions;

[0082] Combine industry standards and competitor data to automatically generate a comparison report with the industry average level, point out financial advantages and disadvantages, and provide strategic suggestions for improving performance;

[0083] Based on the financial analysis report, use the local outlier factor algorithm to detect anomalies in financial data and identify abnormal fluctuations or potential fraud behaviors in the financial data;

[0084] A risk assessment report automatically generated in combination with potential financial risks, and relevant optimization measures are recommended.

[0085] Furthermore, the decision recommendation module combines industry standards, competitor data, and financial analysis reports to automatically generate a comparison report with the industry average level, helping enterprises discover financial advantages and disadvantages and providing accurate financial decision-making suggestions for users.

[0086] Embodiment 2:

[0087] Please refer to Figure 1 As shown, this embodiment is basically the same as the above embodiment. The difference is that the push and feedback module is also used to push personalized financial analysis reports to different users by combining content-based recommendation algorithms and collaborative filtering algorithms;

[0088] Automatically adjust the push order according to the urgency and timeliness of the report through the priority push algorithm;

[0089] Utilize time windows and user schedules to push high-priority reports and avoid conflicts between report pushing and other work of users;

[0090] Collect feedback information from users on financial analysis reports through user behavior tracking and feedback mechanisms;

[0091] After each push, adjust the report generation strategy according to the feedback information through reinforcement learning algorithms.

[0092] Furthermore, the push and feedback module automatically pushes customized financial analysis reports according to user needs, adjusts the push order through the priority push algorithm, and avoids conflicts between report pushing and other work of users. At the same time, through user behavior tracking and feedback mechanisms, optimize the report content according to the feedback information, and continuously improve the report quality and the ability to meet user needs.

[0093] The monitoring and warning module is also used to use the Apache Flink stream processing framework to obtain financial data from multiple data sources in real time and perform real-time analysis;

[0094] Introduce a composite rule engine to achieve different types of risk warnings triggered based on user-set rules;

[0095] Continuously optimize user-set rules through a self-learning mechanism to respond to possible financial risks in the first place;

[0096] Integrate a collaborative work platform to send risk warnings to relevant personnel in real time and adjust operation strategies or financial decisions.

[0097] Furthermore, the monitoring and early warning module adopts real-time stream processing technology and self-learning mechanism to monitor the changes in financial data in real time and automatically issue risk warnings according to the rules set by users, ensuring that financial risks can be discovered and addressed in a timely manner and safeguarding the financial security of the enterprise.

[0098] The RPA automation module is also used to automatically proofread the generated reports using deep learning and natural language processing technologies, checking the compliance, logic, and format of the report content;

[0099] The distribution and viewing of financial analysis reports are restricted to specific personnel through RPA automation control permissions, and the reading status of the reports is monitored;

[0100] The report content is updated through a timed update or incremental update mechanism, and the updated content is tracked and managed through a version control system.

[0101] Furthermore, the RPA automation module can automate the generation, content update, review, and distribution of financial reports, reducing manual intervention, ensuring the compliance, logic, and format uniformity of the reports, and improving the management efficiency of the reports.

[0102] Enterprise application example:

[0103] A medium-sized manufacturing enterprise (hereinafter referred to as "the enterprise") is facing increasingly complex financial data management problems. Traditional financial analysis methods rely on manual intervention, which not only takes a long time but is also prone to errors. In the process of the enterprise's continuous expansion and business development, how to improve financial management efficiency and decision-making quality has become an urgent problem to be solved. To address these issues, the enterprise decided to introduce an intelligent financial analysis report automatic generation system to automatically collect, clean, and analyze financial data through this system, generate accurate financial analysis reports, and help the management optimize business strategies through intelligent decision-making recommendations.

[0104] Implementation steps:

[0105] The enterprise's financial data comes from multiple sources, including ERP systems, financial management systems, external market data, etc. The data management module connects to these systems through API interfaces to automatically collect financial data. Then it automatically detects and corrects anomalies and missing values in the data and ensures data consistency through time series analysis.

[0106] The cleaned financial data enters the financial analysis module, and the data is analyzed multi-dimensionally based on artificial intelligence technology. The FP-growth algorithm is used to identify the correlation relationships between financial data, and potential risks are predicted and analyzed through machine learning. Based on the analysis results, a financial analysis report covering the company's financial status, trend prediction, financial health analysis, etc. is generated using ChatGPT technology.

[0107] While generating the report, based on the analysis results and combined with the financial data of companies in the same industry, the decision recommendation module conducts a comparative analysis through the DBSCAN clustering algorithm. It automatically identifies industry benchmark companies and provides improvement measures and optimization suggestions for the enterprise. For example, the report may point out deficiencies in the enterprise's cost control and provide specific cost reduction suggestions. A financial risk assessment report is generated based on the financial data to identify potential financial risks and provide targeted risk control suggestions.

[0108] In addition, enterprise management can, according to specific needs, select specific financial indicators and analysis dimensions through the report customization module to generate a customized report that meets the actual needs. And through visualization technology, the financial report is presented in a graphical form, facilitating management to quickly understand and make decisions. In addition, the report supports export in multiple formats for the use of different departments.

[0109] After the financial report is generated, the push and feedback module will automatically push the report to relevant management personnel, such as the Chief Financial Officer, CEO, etc., according to the priority. Using a content-based recommendation algorithm, personalized reports are pushed to different users to ensure that key personnel can obtain the report in a timely manner and make decisions. At the same time, through the user behavior tracking and feedback mechanism, the report content is continuously optimized to ensure that the report always meets the user's needs.

[0110] In the above process, the RPA automation module controls the entire process of financial report generation. The generation, review, update, and distribution of the report are completely controlled by the system, avoiding manual intervention and improving work efficiency. At the same time, through deep learning and natural language processing technologies, the report content is automatically reviewed to ensure that the report is compliant, accurate, and in a standardized format.

[0111] The monitoring and early warning module monitors the financial data in real time. Through the Apache Flink stream processing framework, the data is analyzed in real time, and a composite rule engine is used to trigger risk warnings. For example, when a certain financial indicator exceeds the preset threshold, a risk alarm is automatically issued to notify relevant personnel to adjust the operation strategy or financial decision in a timely manner to avoid potential financial risks.

[0112] As can be seen from the above, the present invention automatically collects and cleans financial data from multiple data sources, uses artificial intelligence technology to perform multi-dimensional analysis on the cleaned data, automatically identifies potential patterns, generates high-quality financial analysis reports, and provides accurate financial insights; users can flexibly select financial indicators and analysis dimensions according to enterprise needs to generate personalized reports, ensuring that the content highly matches the enterprise needs; at the same time, through intelligent analysis, personalized financial decision-making suggestions and trend predictions are provided for the enterprise to help the enterprise make more scientific decisions.

[0113] In addition, the system automates the process of generating financial reports, from content update, review to distribution, greatly improving work efficiency and reducing the risk of human intervention. The reports are automatically pushed to relevant personnel and continuously optimized based on feedback information, enhancing the practicality and accuracy of the reports. Financial data is monitored in real time, and early warnings are automatically issued when potential risks are detected, helping enterprises respond to financial risks in a timely manner.

[0114] Embodiment 3:

[0115] The embodiment of the present invention also provides a computer-readable storage medium. A program of an intelligent financial analysis report automatic generation system as described in any one of the above is stored on the computer-readable storage medium. When the program is executed by a processor, it realizes each process of the above automatic generation system embodiment and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0116] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0117] In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments of the present invention are involved. Other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0118] The flowcharts shown in the drawings are only illustrative examples, and do not necessarily include all the contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may change according to the actual situation.

[0119] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent financial analysis report automatic generation system, characterized in that: include: Data management module, used to automatically collect relevant financial data from multiple data sources, and perform data cleaning and preprocessing on the collected data; A financial analysis module, used to perform multi-dimensional analysis on the cleaned financial data based on artificial intelligence technology, identify potential patterns in the financial data, and generate a financial analysis report; An RPA automation module is used to automate each step of generating the financial analysis report using RPA technology, and to realize automated processing of content updating, data review, and report distribution of the financial analysis report; The report customization module is used to provide options for specific financial indicators and analysis dimensions based on the characteristics of the enterprise, and generate customized reports that meet user needs; A decision recommendation module, used to provide personalized financial decision suggestions and trend forecasts based on the financial analysis report; A push and feedback module, used to automatically push the financial analysis report to relevant personnel according to the user's needs, and optimize and adjust the content of the financial analysis report according to the feedback information; The monitoring and early warning module is used to monitor the financial data in real time and automatically issue risk warnings according to user-defined rules.

2. According to claim 1, the intelligent financial analysis report automatic generation system is characterized in that: The data management module is also used for: Connect with financial management system, ERP system and external data source through API interface and database connection to automatically collect the financial data; Using machine learning algorithms to automatically detect and process missing data, correct outliers, and integrate financial data from multiple data sources into a unified structure; A time series analysis algorithm is used to perform consistency check on the financial data and eliminate data inconsistency problems caused by time deviation.

3. The intelligent financial analysis report automatic generation system according to claim 2 is characterized in that: The financial analysis module is also used to: Use the FP-growth algorithm to mine association rules on the cleaned financial data, analyze the correlation between different financial indicators, and discover potential influencing factors; Based on the influencing factors, machine learning and data mining techniques are used to perform predictive analysis on the financial data, calculate the risk score of the financial data, and identify potential financial risks; Where β0, β1, ..., β n is the risk factor, X1,X2,...,X n is the characteristics of the financial data; The data segmentation method is adopted to segment the third-level titles and numbers of the financial analysis report as nodes to construct questions and answers; Integrate ChatGPT and combine GPTs functions to use the constructed questions and answers for training, understand and summarize the financial data, and extract analysis results; Based on the analysis results, ChatGPT generates structured report content and automatically adds data interpretation, trend forecast, and financial health analysis information; Adaptive algorithms are introduced to dynamically adjust the analysis model according to the business needs of different enterprises, identify potential risks and opportunities in real time, and provide corresponding financial analysis reports.

4. The intelligent financial analysis report automatic generation system according to claim 3 is characterized in that: The report customization module is also used to: Based on the financial analysis report, visualization technology is used to present it in a graphical form, and report exports in multiple formats are provided; Customize the content, format, time period and specific indicators of the financial analysis report according to different user needs; Integrate natural language processing technology to provide customized demand functions through voice or text input, use TexRank algorithm for text summarization and extraction, and automatically generate corresponding customized report content: In the formula, S(v j ) is the word v j The importance of jy is the word v j and v y The edge weight between them, d is the damping factor, N(v j ) is the word v j Neighbors.

5. The intelligent financial analysis report automatic generation system according to claim 4 is characterized in that: The decision recommendation module is also used for: Compare and analyze the financial analysis report with other companies in the same industry through the DBSCAN clustering algorithm, identify the financial status of benchmark companies in the same industry, and provide improvement measures and optimization suggestions; Combine industry standards and competitor data to automatically generate a comparison report with the industry average, point out financial strengths and weaknesses, and provide strategic recommendations for improving performance; Based on the financial analysis report, a local anomaly factor algorithm is used to perform anomaly detection on the financial data to identify abnormal fluctuations or potential fraud in the financial data; Where N k (q) is the k neighbors of point q, lrd is the local reachability density; A risk assessment report is automatically generated based on the potential financial risks and relevant optimization measures are recommended.

6. The intelligent financial analysis report automatic generation system according to claim 5 is characterized in that: The push and feedback module is also used for: Pushing personalized financial analysis reports to different users by combining content-based recommendation algorithms and collaborative filtering algorithms; The priority push algorithm automatically adjusts the push order according to the urgency and timeliness of the report; Use time windows and user schedules to push high-priority reports and avoid conflicts between report push and other user work; Collect user feedback on the financial analysis report through user behavior tracking and feedback mechanism; After each push, the report generation strategy is adjusted according to the feedback information through the reinforcement learning algorithm: Where Q(s,a) represents the expected return of taking action a in state s, α is the learning rate, and γ is the discount factor.

7. The intelligent financial analysis report automatic generation system according to claim 6 is characterized in that: The monitoring and early warning module is also used for: Using the Apache Flink stream processing framework to acquire the financial data from multiple data sources in real time and perform real-time analysis; Introducing a composite rule engine to trigger different types of risk warnings based on the user-set rules; Continuously optimize the user-set rules through a self-learning mechanism to deal with possible financial risks in the first place; An integrated collaborative work platform can send risk warnings to relevant personnel in real time to adjust operational strategies or financial decisions.

8. The intelligent financial analysis report automatic generation system according to claim 7 is characterized in that: The RPA automation module is also used to: Use deep learning and natural language processing technologies to automatically review generated reports and check the compliance, logic, and format of report content; Through RPA automated control permissions, the distribution and viewing of the financial analysis report is limited to specific personnel, and the reading status of the report is monitored; The report content is updated through scheduled updates or incremental update mechanisms, and the updated content is tracked and managed through the version control system.

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