Financial data automatic classification system and method based on custom label

Abstract

The invention provides a financial data automatic classification system and method based on custom tags, and the system comprises a user custom tag setting module which is used for a user to set custom tags according to the financial demands and habits of the user to identify different types of financial data; the financial data input module is used for receiving financial data input by a user in various modes including manual input and file import; the AI classification processing module is used for automatically classifying and processing the input financial data based on user-defined labels and deep learning and machine learning technologies; and the result output module provides a visual interface to display a classification report and a data analysis chart, and allows a user to export or share a result. According to the method, the system can more accurately classify the financial data in a mode of combining the user-defined label and the AI algorithm. And the user can set a specific label according to own service requirements, so that the classification is more suitable for an actual application scene.

Description

Technical Field

[0001] The present invention relates to the technical field of financial management, and in particular to a system and method for automatically classifying financial data based on user-defined tags. Background Art

[0002] With the rapid development of science and technology, the processing and management of financial data has become increasingly complex. Traditional financial management mainly relies on manual operations, including data entry, classification, verification and other links. This processing method is not only inefficient, but also prone to errors, especially when faced with large and complex financial data. In addition, manual processing can hardly guarantee the consistency and accuracy of data, and it also increases the manpower costs of enterprises.

[0003] In order to meet these challenges, some automated financial management systems have appeared on the market, which usually use computer software to improve the speed and accuracy of data processing; however, existing systems often lack flexibility and cannot adapt well to the personalized needs of different users; for example, many systems use fixed classification standards, and users cannot customize classification labels according to their specific business needs, which limits the scope of application and practicality of the system.

[0004] Deficiencies of existing technology: 1. Fixed classification standards: Existing financial management systems usually use preset classification standards. Users cannot customize labels according to their own business characteristics and financial management needs, resulting in inflexible and inaccurate classification.

[0005] 2. Inefficient data processing: Traditional manual processing methods are inefficient when processing large amounts of financial data, prone to errors, and require a lot of time and human resources.

[0006] 3. Lack of personalized services: Most current financial management systems provide standardized services and lack customized functions for users' specific needs, such as personalized label settings and data analysis reports.

[0007] 4. Limited application of AI algorithms: Although some systems have begun to introduce AI algorithms for data processing, they are limited in their ability to learn user habits and continuously optimize, and fail to fully utilize the potential of AI to improve classification accuracy and user experience.

[0008] Therefore, the prior art has deficiencies and needs further improvement. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a system and method for automatically classifying financial data based on user-defined tags.

[0010] To achieve the above object, the specific scheme of the present invention is as follows: The present invention provides a financial data automatic classification system based on user-defined tags, the system comprising: The user-defined label setting module is used by users to set customized labels to identify different types of financial data according to their own financial needs and habits, and supports users to add, modify or delete these labels; The financial data input module is used to receive financial data input by users through various methods including manual input and file import, and to perform preliminary verification on the input data to ensure the accuracy and completeness of the data; The AI ​​classification processing module automatically classifies and processes input financial data based on user-defined labels and deep learning and machine learning technologies, and provides decision support for users through in-depth analysis of classification results, including statistical analysis and trend analysis; The result output module provides an intuitive interface to display classification reports and data analysis charts, while allowing users to export or share results.

[0011] Furthermore, the user-defined label setting module also includes a preset transaction classification unit, which corresponds different transaction classifications to different labels, and users can freely adjust these preset classifications and their corresponding labels according to their own needs.

[0012] Furthermore, the financial data input module also supports the recognition of paper bills and electronic invoices, and can obtain detailed information of the bills by taking photos or directly reading the electronic invoice information, and generate corresponding financial data records accordingly.

[0013] Furthermore, the AI ​​classification processing module also includes a feedback unit, which allows the user to accurately match the identification position after confirming the accuracy of the data, and feed back such marked data as training samples to the AI ​​classification processing module to continuously optimize the model performance.

[0014] Furthermore, the result output module also provides a real-time notification function, and when a new classification result is generated, a reminder is sent to the user via email or other instant messaging tools including text messages.

[0015] The present invention also provides a method for automatically classifying financial data based on user-defined tags. Based on the above classification system, the method comprises the following steps: S1, provides a graphical user interface for users to set custom labels to identify different types of financial data, and supports users to add, modify or delete labels; S2, receiving and storing financial data provided by users through various methods including manual input and file import, and performing preliminary verification of the data; S3, uses AI algorithms to automatically classify financial data based on user-defined tags; S4, performs in-depth analysis on the classified financial data to discover the patterns behind the data; S5, outputs the classification results and related data analysis to users in an intuitive way of reports or charts, and allows users to export or share the results; S6, after the user confirms the accuracy of the data, the exact match flag is set; S7, feeds the collected data back to the AI ​​classification processing module to continuously improve its prediction ability.

[0016] Furthermore, in step S1, during the process of setting a custom label, by presetting a transaction classification unit, the user can quickly create a label system according to his or her own needs.

[0017] Furthermore, step S2, the step of receiving financial data also includes a process of extracting information from paper bills or electronic invoices, and automatically filling in relevant financial data fields through image recognition technology.

[0018] Furthermore, step S3, in the process of automatic classification using AI algorithm, also includes using natural language processing technology to parse text descriptions in financial data to improve classification accuracy.

[0019] Furthermore, step S5, the step of outputting the classification results also includes generating a visual report, including a pie chart and a bar chart, so that the user can understand the distribution of the financial data more intuitively.

[0020] The technical solution of the present invention has the following beneficial effects: 1. Improve classification accuracy: By combining user-defined tags with AI algorithms, the system can more accurately classify financial data. Users can set specific tags according to their business needs, making the classification more suitable for actual application scenarios.

[0021] By learning from large amounts of data, AI algorithms can continuously optimize classification models and further improve classification accuracy and efficiency.

[0022] 2. Enhanced flexibility and personalization: Users can freely add, modify or delete tags according to their own needs, thereby meeting the personalized financial management needs of different users.

[0023] The system supports multiple input methods (such as manual input, file import, etc.), which improves the flexibility and convenience of data entry.

[0024] 3. Improve processing efficiency: The automated classification and processing process greatly reduces the need for human intervention and improves the speed and efficiency of financial data processing.

[0025] By taking photos to identify or directly reading information from electronic invoices, the data entry process is simplified and human errors are reduced.

[0026] 4. Provide decision support: The AI ​​classification processing module can not only classify data, but also conduct in-depth analysis of the classification results, such as statistical analysis, trend analysis, etc., to provide users with valuable data insights and decision support.

[0027] The intelligent recommendation function can help users predict future financial activity trends and provide personalized financial planning advice.

[0028] 5. Continuous learning and optimization: The dynamic learning module can continuously adjust the AI ​​classification algorithm based on user behavior patterns and feedback, and optimize the accuracy of financial data classification through an adaptive learning mechanism.

[0029] The feedback mechanism after users confirm the accuracy of the data helps the AI ​​model to continuously improve its predictive ability and achieve self-improvement of the system.

[0030] 6. Intuitive results display: The result output module provides an intuitive interface that displays classification reports and data analysis charts, allowing users to quickly understand the distribution of financial data.

[0031] Allow users to export or share results to facilitate interaction and sharing with other systems or individuals.

[0032] These beneficial effects combined have significantly improved the automation level, accuracy and user experience of financial data management, providing businesses and individuals with more efficient, flexible and secure financial management tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is the automatic classification process of the bill label of the present invention; Figure 2 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments; it can be understood that the specific embodiments described herein are only used to explain the present invention rather than to limit the present invention; it should also be noted that, for ease of description, only the parts related to the present invention rather than all are shown in the accompanying drawings.

[0035] Combination Figure 1-Figure 2As shown, the present invention provides a financial data automatic classification system based on custom tags, the system comprising: The user-defined label setting module is used by users to set customized labels to identify different types of financial data according to their own financial needs and habits, and supports users to add, modify or delete these labels; The financial data input module is used to receive financial data input by users through various methods including manual input and file import, and to perform preliminary verification on the input data to ensure the accuracy and completeness of the data; The AI ​​classification processing module automatically classifies and processes input financial data based on user-defined labels and deep learning and machine learning technologies, and provides decision support for users through in-depth analysis of classification results, including statistical analysis and trend analysis; The result output module provides an intuitive interface to display classification reports and data analysis charts, while allowing users to export or share results.

[0036] The user-defined label setting module also includes a preset transaction classification unit, which corresponds different transaction classifications to different labels. Users can freely adjust these preset classifications and their corresponding labels according to their own needs.

[0037] The financial data input module also supports the recognition of paper bills and electronic invoices, and can obtain the detailed content of the bill by taking photos or directly reading the electronic invoice information, and generate corresponding financial data records accordingly.

[0038] The AI ​​classification processing module also includes a feedback unit that allows the user to accurately match the identification position after confirming the accuracy of the data, and feeds such labeled data back to the AI ​​classification processing module as training samples to continuously optimize the model performance.

[0039] The result output module also provides a real-time notification function, and when a new classification result is generated, a reminder is sent to the user via email or other instant messaging tools including text messages.

[0040] The present invention also provides a method for automatically classifying financial data based on user-defined tags. Based on the above classification system, the method comprises the following steps: S1, provides a graphical user interface for users to set custom labels to identify different types of financial data, and supports users to add, modify or delete labels; S2, receiving and storing financial data provided by users through various methods including manual input and file import, and performing preliminary verification of the data; S3, uses AI algorithms to automatically classify financial data based on user-defined tags; S4, performs in-depth analysis on the classified financial data to discover the patterns behind the data; S5, outputs the classification results and related data analysis to users in an intuitive way of reports or charts, and allows users to export or share the results; S6, after the user confirms the accuracy of the data, the exact match flag is set; S7, feeds the collected data back to the AI ​​classification processing module to continuously improve its prediction ability.

[0041] Step S1, in the process of setting custom labels, by presetting transaction classification units, users can quickly create a label system according to their own needs.

[0042] Step S2, the step of receiving financial data also includes a process of extracting information from paper bills or electronic invoices, and automatically filling in relevant financial data fields through image recognition technology.

[0043] Step S3, in the process of automatic classification using AI algorithms, also includes using natural language processing technology to parse text descriptions in financial data to improve classification accuracy.

[0044] Step S5, the step of outputting the classification results also includes generating a visual report, including a pie chart and a bar chart, so that the user can understand the distribution of the financial data more intuitively.

[0045] Example: The present invention provides an automatic classification system for financial data based on user-defined tags, which realizes automatic classification and processing of financial data through user-defined tags and AI algorithms.

[0046] Automatic classification process of bill labels: start: Photo recognition / electronic invoice display bill details result: At this stage, users can obtain detailed information about the bill by taking a photo of the paper bill or viewing the electronic invoice.

[0047] After receiving the recognition results, the backend calls the AI ​​algorithm based on the recognition results to obtain the transaction classification: After receiving the recognition results in the backend, the system will use the artificial intelligence algorithm to classify the transactions.

[0048] Enter the Tickets tab page: Users can enter a page dedicated to managing ticket tags.

[0049] Get location information based on transaction classification: Based on the transaction category previously determined, the system will attempt to obtain specific location information related to that type of transaction.

[0050] Whether the venue information is obtained: If the information of the relevant venue is successfully found, the process continues; otherwise, the information needs to be further manually entered.

[0051] Manual entry display ticket details page: If not enough information is automatically obtained, the user may need to manually enter more details.

[0052] The front end calls the AI ​​algorithm interface to obtain transaction classification: In the front-end application, artificial intelligence technology can also be used to help classify transactions.

[0053] Click Submit to confirm data accuracy and set the exact match flag: When the user confirms that all data is correct, they can click the Submit button and mark it as verified.

[0054] Call back the AI ​​algorithm interface for relearning: The final step is to feed the collected data back to the AI ​​model so that it can continuously improve its predictive capabilities.

[0055] End: The whole process ends.

[0056] With the rapid development of science and technology, the processing and management of financial data has become increasingly complex. Traditional financial data processing methods mainly rely on manual work, which is not only inefficient but also prone to errors. In order to solve this problem, the present invention proposes an automatic classification system for financial data based on custom tags. The system uses user-defined tags and AI algorithms to realize automatic classification and processing of financial data, greatly improving the efficiency and accuracy of data processing.

[0057] The purpose of the present invention is to provide an automatic classification system for financial data based on custom labels, which includes a user-defined label setting module, a financial data input module, an AI classification processing module and a result output module.

[0058] 1. User-defined label setting module: This module allows users to set customized labels to identify different types of financial data according to their own financial needs and habits. The system pre-classifies transactions, and different transaction categories correspond to different labels. Users can freely add, modify or delete labels to meet personalized classification needs.

[0059] Figure 1: Default transaction classification data

[0060] 2. Financial data input module: This module is responsible for receiving financial data input by users, including income, expenditure, transfer, etc. Users can easily input and edit financial data through the interface or interface provided by the system.

[0061] 3. AI classification processing module: This module is the core part of the present invention. It automatically classifies and processes the input financial data based on user-defined labels and AI algorithms. The AI ​​algorithm gradually optimizes the accuracy of classification by learning user-defined labels and financial data. At the same time, the module can also conduct in-depth analysis of the classification results, explore the laws behind the data, and provide decision support for users.

[0062] Figure 2: AI automatic labeling rules Figure 2 shows the AI ​​automatic labeling rule diagram, which lists various types of bills and their corresponding transaction categories, expense occurrence time, expense occurrence city, departure and destination, expense occurrence location, transaction content, and seller. Each row represents a bill type, and the corresponding AI automatic labeling rules are listed on the right.

[0063]

[0064] 4. Result output module: This module is responsible for outputting the results of the AI ​​classification processing module to the user. Users can view classification reports, data analysis results, etc. through the interface or interface provided by the system. At the same time, users can also export or share the results as needed.

[0065] Among them, the user-defined label setting module allows users to set customized labels to identify different types of financial data according to their own financial needs and habits.

[0066] The AI ​​classification and processing module automatically classifies and processes input financial data based on user-defined labels and AI algorithms. It can also conduct in-depth analysis of the classification results to explore the patterns behind the data.

[0067] 1. User-defined label setting module: This module provides a user-friendly operation interface through a graphical user interface (GUI), allowing users to customize labels. Users can add, modify or delete labels according to actual needs in order to better manage their financial data.

[0068] 2. Financial data input module: This module supports multiple input methods, such as manual input, file import, etc. Users can easily input and edit financial data through the interface or interface provided by the system. The system will conduct preliminary verification of the input financial data to ensure the accuracy and completeness of the data.

[0069] 3. AI classification processing module: This module uses advanced AI algorithms, such as deep learning and machine learning, to automatically classify and process input financial data. The AI ​​algorithm gradually optimizes the accuracy of classification by learning user-defined labels and financial data. At the same time, the module can also conduct in-depth analysis of the classification results, such as statistical analysis and trend analysis, to provide users with more comprehensive decision support.

[0070] 4. Result output module: This module displays the classification results and data analysis results in an intuitive way. Users can view classification reports, data analysis charts, etc. through the interface provided by the system. At the same time, users can also export or share the results as needed to interact with other people or the system.

[0071] The automatic classification system for financial data based on custom tags described in the present invention realizes the automatic classification and processing of financial data through the combination of user-defined tags and AI algorithms, improves the efficiency and accuracy of data processing, and provides great convenience for users' financial management.

[0072] Working principle: 1. User-defined label settings User interface: The system provides a graphical user interface (GUI) that allows users to set custom labels to identify different types of financial data based on their own financial needs and habits.

[0073] Tag management: Users can freely add, modify or delete tags to meet personalized classification needs. The system may also preset some transaction classification templates to help users quickly create a tag system that suits their needs.

[0074] 2. Financial data input Multiple input methods: The system supports multiple financial data input methods, including manual input, file import, etc.

[0075] Bill recognition: For paper bills, the system can extract bill information through photo recognition technology; for electronic invoices, the system can directly read and parse the content of the electronic invoice.

[0076] Preliminary verification: The system will perform a preliminary verification on the input data to ensure its accuracy and completeness.

[0077] 3. AI algorithm processing Data classification: The AI ​​classification processing module automatically classifies input financial data based on user-defined labels and deep learning and / or machine learning techniques.

[0078] Intelligent analysis: AI algorithms not only classify data, but also conduct in-depth analysis of the classification results, such as statistical analysis, trend analysis, etc., to provide decision support for users.

[0079] Dynamic learning: The system has dynamic learning capabilities and can continuously adjust the AI ​​classification algorithm based on user behavior patterns and feedback. It optimizes the accuracy of financial data classification through an adaptive learning mechanism and can share learning results among different users to improve the overall intelligence level of the system.

[0080] 4. Result output and display Intuitive display: The result output module provides an intuitive interface to display classification reports and data analysis charts, allowing users to quickly understand the distribution of financial data.

[0081] Export and share: Users can export or share classification results to facilitate interaction and sharing with other systems or individuals.

[0082] Real-time notification: The system also provides real-time notification function. When new classification results are generated, reminders can be sent to users via email or other instant messaging tools.

[0083] 5. Data security and privacy protection Encryption technology: The system uses advanced encryption technology and access control strategies to protect the security of users' financial information and prevent unauthorized access and data leakage.

[0084] Transparent reporting: The system provides transparent data usage reports, allowing users to understand how their data is used and protected, and enhancing users' trust in the system.

[0085] 6. Cross-platform synchronization and collaboration Multi-device synchronization: The system supports cross-platform synchronization, allowing users to seamlessly access and manage their financial data and label settings on different devices (such as mobile phones, tablets, and personal computers), ensuring data consistency and real-time updates.

[0086] Multi-user collaboration: The system supports multi-user collaboration, and team members can jointly maintain and review financial information, improving work efficiency and teamwork capabilities.

[0087] Process Overview: start: Bill recognition / electronic invoice display: Users can obtain bill details by taking a photo of a paper bill or viewing an electronic invoice.

[0088] The backend receives the recognition results: After the system backend receives the recognition results, it calls the AI ​​algorithm to classify the transactions.

[0089] Enter the ticket tab page: The user enters a page specifically used to manage ticket tags.

[0090] Obtaining location information: The system attempts to obtain relevant location information based on the transaction classification.

[0091] Confirm venue information: If the venue information is successfully obtained, the process continues; otherwise, further manual input is required.

[0092] Manual Entry: If not enough information is captured automatically, the user may need to enter more details manually.

[0093] Front-end calls AI algorithm interface: Front-end applications can also use artificial intelligence technology to help classify transactions.

[0094] Submit confirmation: After the user confirms that all data is correct, click the Submit button and mark it as verified.

[0095] Callback AI algorithm interface for relearning: The collected data is fed back to the AI ​​model so that it can continuously improve its predictive capabilities.

[0096] End: The whole process ends.

[0097] Through the above process, the system realizes efficient, accurate, flexible and secure automatic classification and processing of financial data, greatly improving the efficiency of financial management and user experience.

[0098] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the protection scope of the present invention.

Claims

1. An automatic classification system for financial data based on custom labels, characterized in that: The system comprises: The user-defined label setting module is used by users to set customized labels to identify different types of financial data according to their own financial needs and habits, and supports users to add, modify or delete these labels; The financial data input module is used to receive financial data input by users through various methods including manual input and file import, and to perform preliminary verification on the input data to ensure the accuracy and completeness of the data; The AI ​​classification processing module automatically classifies and processes input financial data based on user-defined labels and deep learning and machine learning technologies, and provides decision support for users through in-depth analysis of classification results, including statistical analysis and trend analysis; The result output module provides an intuitive interface to display classification reports and data analysis charts, while allowing users to export or share results.

2. The system according to claim 1, characterized in that The user-defined label setting module also includes a preset transaction classification unit, which corresponds different transaction classifications to different labels. Users can freely adjust these preset classifications and their corresponding labels according to their own needs.

3. The system according to claim 1, characterized in that The financial data input module also supports the recognition of paper bills and electronic invoices, and can obtain the detailed content of the bill by taking photos or directly reading the electronic invoice information, and generate corresponding financial data records accordingly.

4. The system according to claim 1, characterized in that The AI ​​classification processing module also includes a feedback unit that allows the user to accurately match the identification position after confirming the accuracy of the data, and feeds such labeled data back to the AI ​​classification processing module as training samples to continuously optimize the model performance.

5. The system according to claim 1, characterized in that The result output module also provides a real-time notification function, and when a new classification result is generated, a reminder is sent to the user via email or other instant messaging tools including text messages.

6. A method for automatically classifying financial data based on custom tags, based on the classification system according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: S1, provides a graphical user interface for users to set custom labels to identify different types of financial data, and supports users to add, modify or delete labels; S2, receiving and storing financial data provided by users through various methods including manual input and file import, and performing preliminary verification of the data; S3, uses AI algorithms to automatically classify financial data based on user-defined tags; S4, performs in-depth analysis on the classified financial data to discover the patterns behind the data; S5, outputs the classification results and related data analysis to users in an intuitive way of reports or charts, and allows users to export or share the results; S6, after the user confirms the accuracy of the data, the exact match flag is set; S7, feeds the collected data back to the AI ​​classification processing module to continuously improve its prediction ability.

7. The method according to claim 6, characterized in that Step S1, in the process of setting custom labels, by presetting transaction classification units, users can quickly create a label system according to their own needs.

8. The method according to claim 6, characterized in that Step S2, the step of receiving financial data also includes a process of extracting information from paper bills or electronic invoices, and automatically filling in relevant financial data fields through image recognition technology.

9. The method according to claim 6, characterized in that Step S3, in the process of automatic classification using AI algorithms, also includes using natural language processing technology to parse text descriptions in financial data to improve classification accuracy.

10. The method according to claim 6, characterized in that Step S5, the step of outputting the classification results also includes generating a visual report, including a pie chart and a bar chart, so that the user can understand the distribution of the financial data more intuitively.

Images