System and method for automatically generating company financial report data based on machine learning

Abstract

The invention discloses a system and method for automatically generating company financial report data based on machine learning, and the system comprises a data integration and cleaning module which is used for integrating and cleaning collected data; the automatic data processing module is used for carrying out standardization and normalization processing on the data; the model selection and training module is used for selecting a machine learning model and training the machine learning model; the report generation module is used for generating a financial report; and the report auditing module is used for auditing the financial report. According to the invention, automatic collection, processing, report generation and auditing of financial data are realized, and the accuracy and efficiency of the financial report are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of machine learning, and specifically is a system and method for automatically generating company financial report data based on machine learning. Background Art

[0002] Currently, the generation of corporate financial reports mainly relies on manual operations, including data collection, data processing, report writing and review. This process is time-consuming and labor-intensive, prone to human errors and inefficient.

[0003] With the rapid development of big data and artificial intelligence technology, more and more companies are beginning to use AI technology to improve work efficiency and accuracy. Especially in the field of financial management, the automatic generation of financial reports has become an important research direction. With the development of big data and machine learning technology, more and more companies are beginning to explore ways to automatically generate financial reports to improve efficiency and accuracy.

[0004] There are currently a variety of technical solutions for automatically generating financial reports on the market, including:

[0005] 1. Template-based method: The structure and format of the report are pre-set, and then the corresponding fields are filled according to the input data. This method is simple and direct, but it is less flexible and difficult to adapt to the specific needs of different companies.

[0006] 2. Rule-based approach: A series of rules are defined to guide the report generation process. Although it can improve flexibility to a certain extent, it is still insufficient when dealing with complex scenarios.

[0007] 3. Machine learning-based methods: Use historical financial data to train the model, and then use the model to predict future financial conditions and generate reports. This method can better capture the relationship between data, but it has high requirements on data quality and poor model interpretability.

[0008] Although existing technical solutions have solved the problem of financial report generation to a certain extent, they still have the following major shortcomings:

[0009] 1. Lack of flexibility: Template- and rule-based approaches are difficult to adapt to the diverse needs of different companies, especially when dealing with complex financial data.

[0010] 2. Strong data dependence: Machine learning-based methods have high requirements on the quality and quantity of data. If the data is incomplete or inaccurate, the generated reports may have large deviations.

[0011] 3. Poor interpretability: Machine learning models are often viewed as black box models, making it difficult to explain the specific logic of how they generate reports, which is particularly important in the financial field.

[0012] 4. High update and maintenance costs: As the business of an enterprise changes, templates and rules need to be adjusted frequently, which increases the maintenance cost of the system. Summary of the invention

[0013] In view of the above problems, the present invention is proposed to provide a system and method for automatically generating company financial report data based on machine learning to overcome the above problems or at least partially solve the above problems.

[0014] To achieve the above object, the present invention adopts the following technical solutions:

[0015] A system for automatically generating company financial report data based on machine learning, the system comprising:

[0016] Data integration and cleaning module, used to integrate and clean the collected data;

[0017] Automated data processing module, used to standardize and normalize data;

[0018] Model selection and training module, used to select and train machine learning models;

[0019] Report generation module, used to generate financial reports;

[0020] Report review module, used to review financial reports.

[0021] Optional data integration and cleaning modules include:

[0022] Data source connection unit, develops API interface to establish data connection with ERP system, bank account and invoice system to automatically obtain financial data;

[0023] The data cleaning unit uses Python and Pandas libraries for data preprocessing and performs outlier detection through machine learning algorithms to ensure the rationality of the data.

[0024] Optionally, the data preprocessing includes removing duplicate values, processing missing values ​​and detecting outliers.

[0025] Optional, automated data processing modules include:

[0026] Data standardization unit, converting data from different sources to the same dimension to ensure data comparability;

[0027] The data classification and label generation unit uses decision trees and random forest algorithms to automatically classify financial data and automatically generates labels for financial data based on preset rules.

[0028] Optional, model selection and training modules include:

[0029] A model selection unit, which selects a machine learning model according to task requirements, wherein the machine learning model includes linear regression, decision tree, random forest and neural network;

[0030] The model training unit uses historical financial data to train the model and adopts the cross-validation method to evaluate the generalization ability of the model.

[0031] Optionally, the report generation module includes:

[0032] The text generation unit uses natural language generation technology to convert the model's prediction results into natural language descriptions to generate the text part of the financial report. It also combines the template engine to generate the structure and content of the report based on the preset report template.

[0033] The chart generation unit uses data visualization tools to automatically generate charts and supports the generation of dynamic charts, allowing users to interact and explore data.

[0034] Optionally, the data visualization tools include Matplotlib, Seaborn and Plotly.

[0035] Optional, report review modules include:

[0036] The draft review unit conducts a preliminary review of the automatically generated report to ensure the logic and accuracy of the report;

[0037] The secondary review unit reviews the initial review report to ensure the completeness of the report;

[0038] The automated review unit uses automated review tools to assist manual review in order to improve review efficiency.

[0039] The present invention also provides a method for automatically generating company financial report data based on machine learning, the method is based on any of the above-mentioned systems, and the method comprises:

[0040] Use API interfaces to collect data from multiple data sources;

[0041] Use machine learning algorithms to clean the collected data;

[0042] Automated data processing using machine learning models;

[0043] Use historical financial data to train machine learning models and predict future financial indicators;

[0044] Use natural language generation technology to convert the model's prediction results into natural language descriptions and generate the text part of the financial report;

[0045] Use data visualization tools to generate charts and graphs for financial reports;

[0046] Perform a preliminary review of automatically generated reports;

[0047] Review the report after the initial review.

[0048] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0049] 1. This invention improves the accuracy and reliability of generated reports and improves the understanding and prediction capabilities of financial data by using advanced AI big models; it can adapt to the specific needs of different companies and process complex financial data.

[0050] 2. The present invention improves the interpretability of the model, so that the generated report has clear logic and explanation, which is convenient for auditing and review; it also reduces the dependence on templates and rules, and reduces the updating and maintenance costs of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a system flow for automatically generating company financial report data based on machine learning provided in Example 1 of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0053] Embodiment 1:

[0054] See also Figure 1 This embodiment provides a system for automatically generating company financial report data based on machine learning, the system comprising:

[0055] The data integration and cleaning module is used to integrate and clean the collected data.

[0056] The data integration and cleaning modules include:

[0057] The data source connection unit develops API interfaces to establish data connections with ERP systems, bank accounts and invoice systems to automatically obtain financial data.

[0058] The data cleaning unit uses Python and Pandas libraries to preprocess the data and detect outliers through machine learning algorithms to ensure the rationality of the data. The data preprocessing includes removing duplicate values, processing missing values, and detecting outliers.

[0059] Automated data processing module, used to standardize and normalize data.

[0060] The automated data processing modules include:

[0061] Data standardization unit converts data from different sources to the same dimension to ensure data comparability.

[0062] The data classification and label generation unit uses decision trees and random forest algorithms to automatically classify financial data, such as automatically classifying income and expenses into different categories. It also automatically generates labels for financial data based on preset rules to facilitate subsequent report generation.

[0063] The model selection and training module is used to select and train machine learning models.

[0064] The model selection and training module includes:

[0065] The model selection unit selects a suitable machine learning model according to task requirements, and the machine learning model includes linear regression, decision tree, random forest and neural network.

[0066] The model training unit uses historical financial data to train the model to ensure the accuracy and stability of the model; and uses cross-validation methods to evaluate the generalization ability of the model to ensure that the model performs well on unseen data.

[0067] Report generation module, used to generate financial reports.

[0068] The report generation module includes:

[0069] The text generation unit uses natural language generation technology to convert the model's prediction results into natural language descriptions to generate the text part of the financial report, and combines it with the template engine to generate the structure and content of the report based on the preset report template.

[0070] The chart generation unit automatically generates charts using data visualization tools, and supports the generation of dynamic charts, allowing users to interact and explore data. The data visualization tools include Matplotlib, Seaborn, and Plotly.

[0071] Report review module, used to review financial reports.

[0072] The report review module includes:

[0073] The draft review unit is where professional financial personnel conduct a preliminary review of the automatically generated reports to ensure the logic and accuracy of the reports.

[0074] In the secondary review unit, another professional will review the initial review report to ensure the completeness of the report.

[0075] The automated review unit introduces automated review tools to assist manual review in order to improve review efficiency.

[0076] This embodiment improves the accuracy and reliability of generated reports and improves the understanding and prediction capabilities of financial data by using advanced AI big models; it can adapt to the specific needs of different companies and process complex financial data.

[0077] This embodiment improves the interpretability of the model, so that the generated report has clear logic and explanation, which is convenient for auditing and review; it also reduces the dependence on templates and rules, and reduces the update and maintenance costs of the system.

[0078] Embodiment two:

[0079] Embodiment 2 discloses a method for automatically generating company financial report data based on machine learning. The method is based on the above-mentioned system for automatically generating company financial report data based on machine learning. The method comprises the following steps:

[0080] S1. Collect data from multiple data sources using API interfaces.

[0081] S2. Use machine learning algorithms to clean the collected data.

[0082] S3. Use machine learning models to automate data processing.

[0083] S4. Use historical financial data to train machine learning models and predict future financial indicators.

[0084] S5. Use natural language generation technology to convert the model's prediction results into natural language descriptions and generate the text part of the financial report.

[0085] S6. Use data visualization tools to generate charts for financial reports.

[0086] S7. Conduct a preliminary review of the automatically generated report.

[0087] S8. Review the report after the preliminary review.

[0088] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A system for automatically generating company financial report data based on machine learning, characterized in that: The system comprises: Data integration and cleaning module, used to integrate and clean the collected data; Automated data processing module, used to standardize and normalize data; Model selection and training module, used to select and train machine learning models; Report generation module, used to generate financial reports; Report review module, used to review financial reports.

2. A system for automatically generating company financial report data based on machine learning as claimed in claim 1, characterized in that: The data integration and cleaning modules include: Data source connection unit, develops API interface to establish data connection with ERP system, bank account and invoice system to automatically obtain financial data; The data cleaning unit uses Python and Pandas libraries for data preprocessing and performs outlier detection through machine learning algorithms to ensure the rationality of the data.

3. A system for automatically generating company financial report data based on machine learning as claimed in claim 2, characterized in that: The data preprocessing includes removing duplicate values, processing missing values ​​and detecting outliers.

4. The system for automatically generating company financial report data based on machine learning as claimed in claim 1, characterized in that: The automated data processing modules include: Data standardization unit, converting data from different sources to the same dimension to ensure data comparability; The data classification and label generation unit uses decision trees and random forest algorithms to automatically classify financial data and automatically generates labels for financial data based on preset rules.

5. The system for automatically generating company financial report data based on machine learning as claimed in claim 1, characterized in that: The model selection and training module includes: A model selection unit, which selects a machine learning model according to task requirements, wherein the machine learning model includes linear regression, decision tree, random forest and neural network; The model training unit uses historical financial data to train the model and adopts the cross-validation method to evaluate the generalization ability of the model.

6. The system for automatically generating company financial report data based on machine learning as claimed in claim 1, characterized in that: The report generation module includes: The text generation unit uses natural language generation technology to convert the model's prediction results into natural language descriptions to generate the text part of the financial report. It also combines the template engine to generate the structure and content of the report based on the preset report template. The chart generation unit uses data visualization tools to automatically generate charts and supports the generation of dynamic charts, allowing users to interact and explore data.

7. A system for automatically generating company financial report data based on machine learning as claimed in claim 6, characterized in that: The data visualization tools include Matplotlib, Seaborn and Plotly.

8. The system for automatically generating company financial report data based on machine learning as claimed in claim 1, characterized in that: The report review module includes: The draft review unit conducts a preliminary review of the automatically generated report to ensure the logic and accuracy of the report; The secondary review unit reviews the initial review report to ensure the completeness of the report; The automated review unit uses automated review tools to assist manual review in order to improve review efficiency.

9. A method for automatically generating company financial report data based on machine learning, the method is based on the system for automatically generating company financial report data based on machine learning according to any one of claims 1 to 8, characterized in that: The method comprises: Use API interfaces to collect data from multiple data sources; Use machine learning algorithms to clean the collected data; Automated data processing using machine learning models; Use historical financial data to train machine learning models and predict future financial indicators; Use natural language generation technology to convert the model's prediction results into natural language descriptions and generate the text part of the financial report; Use data visualization tools to generate charts and graphs for financial reports; Perform a preliminary review of automatically generated reports; Review the report after the initial review.

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