Enterprise financial data proofreading method and system and storage medium

Abstract

The invention belongs to the field of enterprise financial analysis, and particularly relates to an enterprise financial data proofreading method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring financial data from a financial system of an enterprise, and preprocessing the financial data, including data cleaning, format conversion, data standardization and the like, so as to ensure the consistency and comparability of the data; s2, feature extraction and selection: extracting features including numerical features, text features, time features and the like from the preprocessed financial data, and selecting key features according to the features of the data and proofreading requirements; and S3, machine learning model construction: constructing machine learning models, including a classification model, a regression model, a clustering model and the like, based on the extracted features. According to the invention, rapid and accurate proofreading of financial data can be realized, an enterprise can better master own financial conditions and operation conditions, and powerful support is provided for decision and management of the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise financial analysis, and in particular to an enterprise financial data proofreading method, system and storage medium. Background Art

[0002] Corporate financial data is an important foundation for corporate operations and management. It reflects the economic status, operational efficiency and profitability of the company. With the expansion of corporate scale and the complexity of business, the accuracy and timeliness of financial data are crucial for corporate decision-making, cost control, risk management and other aspects. However, due to the wide range of sources, diverse formats and complex processing procedures of financial data, data errors and anomalies are difficult to avoid. Therefore, financial data proofreading has become a key link in ensuring data quality.

[0003] At present, the proofreading of corporate financial data mainly relies on manual review and simple computer program verification. Although manual review can detect some complex data errors and anomalies, it is inefficient and easily affected by human factors, making it difficult to ensure the comprehensiveness and accuracy of data proofreading. Simple computer program verification, such as format verification and range verification, can improve the efficiency of proofreading, but it is often unable to effectively identify some hidden data errors and anomalies, such as data entry errors, data calculation errors, data logic errors, etc.

[0004] The existing financial data proofreading technology has the following main deficiencies: Lack of intelligence: Most existing technologies use fixed rules and algorithms for proofreading, lack intelligent learning and adaptability, and are difficult to deal with complex and changeable data errors and anomalies. Low efficiency: Both manual review and simple computer program verification have efficiency problems and are difficult to meet the needs of large-scale financial data proofreading. Lack of accuracy: Due to the limitations of existing technologies, it is often impossible to fully and accurately identify and process errors and anomalies in financial data. Poor scalability: Existing technologies often lack flexibility and scalability, and are difficult to adapt to the development and changes of corporate business. Therefore, we propose an enterprise financial data proofreading method, system and storage medium to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method, system and storage medium for checking corporate financial data.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for checking enterprise financial data, comprising the following steps:

[0008] S1: Data collection and preprocessing: Collect financial data from the company's financial system and perform preprocessing, including data cleaning, format conversion, data standardization, etc., to ensure data consistency and comparability;

[0009] S2: Feature extraction and selection: Extract features from preprocessed financial data, including numerical features, text features, time features, etc., and select key features based on the characteristics of the data and proofreading requirements;

[0010] S3: Machine learning model construction: Based on the extracted features, machine learning models are constructed, including classification models, regression models, clustering models, etc., to identify and process errors and anomalies in financial data;

[0011] S4: Data proofreading and verification: Use the constructed machine learning model to proofread the financial data, including data consistency check, logical relationship verification, outlier detection, etc., and generate a proofreading report. At the same time, verify the proofreading results to ensure the accuracy and reliability of the proofreading;

[0012] S5: Result output and feedback: Output the proofreading results to the user and provide a feedback mechanism to allow the user to confirm, modify or supplement the proofreading results to further improve the accuracy of the data;

[0013] S6: System optimization and upgrade: Based on user feedback and actual needs, the system is optimized and upgraded, including algorithm improvement, model update, function expansion, etc., to continuously improve the performance and effect of the system.

[0014] Optionally, S1: Data collection and preprocessing includes the following steps: Data collection: Collect financial data from the company's financial system, including financial statements, vouchers, account books, etc.; Data cleaning: Remove redundant information, invalid information and outliers in the data to ensure the consistency and comparability of the data; Format conversion: Convert data in different formats into a unified format for subsequent processing and analysis; Data standardization: Standardize the data, such as unifying the amount unit and date format, to eliminate differences and conflicts between data.

[0015] Optionally, the feature extraction and selection of S2 includes the following steps; numerical feature extraction: extracting numerical features from financial data, such as amount, proportion, growth rate, etc., for analyzing the quantitative relationship and changing trend of the data; text feature extraction: extracting text features from financial data, such as account name, summary information, etc., for analyzing the semantics and logical relationship of the data; time feature extraction: extracting time features from financial data, such as date, time interval, etc., for analyzing the time distribution and changing patterns of the data; feature selection: selecting key features based on the characteristics of the data and proofreading requirements for building a machine learning model.

[0016] Optionally, the data proofreading and verification of S4 includes the following steps; Model selection: select a suitable machine learning model, such as a classification model, regression model, clustering model, etc., according to the characteristics of the data and the proofreading requirements; Model training: train the model using the extracted features and labeled data sets so that the model can learn the patterns and characteristics of the data; Model evaluation: evaluate the model through cross-validation, accuracy, recall and other indicators to ensure the performance and effectiveness of the model.

[0017] Optionally, the data proofreading and verification includes the following steps: data consistency check: using a machine learning model to perform a consistency check on the financial data, such as checking whether debits and credits are balanced, whether the account balance is correct, etc.; logical relationship verification: using a machine learning model to verify the logical relationship between financial data, such as checking whether the data in the income statement conforms to the logical relationship; outlier detection: using a machine learning model to detect outliers in the financial data, such as checking whether there are abnormally large amounts or abnormal proportions, etc.; generating a proofreading report: generating a proofreading report based on the proofreading results, including an error list, abnormal prompts, suggested measures, etc.

[0018] Optionally, the S5 result output and feedback includes the following steps: result output: outputting the proofreading results to the user, including an error list, abnormal prompts, etc.; user feedback: allowing the user to confirm, modify or supplement the proofreading results to further improve the accuracy of the data; result optimization: optimizing and adjusting the proofreading results based on user feedback and actual needs to improve the accuracy and reliability of the data.

[0019] Optionally, the S6 system optimization and upgrade includes the following steps: algorithm improvement: improve and optimize the machine learning algorithm based on user feedback and actual needs to improve the performance and effect of the model; model update: regularly update the machine learning model to adapt to different scenarios and changes in needs; function expansion: expand the system's functions based on user's actual needs, such as adding data visualization, data analysis and other functions.

[0020] In the second aspect, the present invention provides an enterprise financial data proofreading system, including a user interface layer module: providing a user interaction interface for inputting proofreading requests, viewing proofreading results and providing user feedback; a business logic layer module: including modules such as data collection, preprocessing, feature extraction, machine learning model construction, data proofreading and result output, to realize intelligent proofreading of enterprise financial data; a data storage layer module: storing raw data, feature data, model data, proofreading results and other data to provide data support for the system.

[0021] In a third aspect, the present invention provides a storage medium for checking enterprise financial data, wherein the storage medium is burned with a computer program, and the computer program implements the above method when running in the memory of a server.

[0022] Beneficial effects of the present invention:

[0023] 1. By adopting machine learning and big data analysis technology, it is possible to quickly and accurately proofread financial data. Compared with traditional manual review and simple computer program verification, the proofreading method of the present invention has higher efficiency and accuracy, and can significantly improve the efficiency and quality of data proofreading.

[0024] 2. By adopting modular and configurable design ideas, the system's functions and performance can be easily expanded. At the same time, the system supports real-time proofreading and dynamic adjustment functions, and can be flexibly configured and adjusted according to different scenarios and needs to adapt to the development and changes of corporate business;

[0025] 3. By adopting intelligent proofreading methods, it can automatically identify and process errors and anomalies in financial data, reducing the cost and risk of manual intervention. At the same time, the system provides a user feedback mechanism, allowing users to confirm, modify or supplement the proofreading results, further improving the accuracy and reliability of the data;

[0026] 4. The implementation of the present invention can significantly improve the financial management level of enterprises, including improving data quality, optimizing decision support, strengthening risk management, etc. By realizing fast and accurate proofreading of financial data, enterprises can better understand their own financial status and operation conditions, and provide strong support for the decision-making and management of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is an enterprise financial data proofreading system architecture diagram of an enterprise financial data proofreading method, system and storage medium proposed by the present invention;

[0028] Figure 2 A flowchart of enterprise financial data proofreading method, system and storage medium for enterprise financial data proofreading proposed by the present invention;

[0029] Figure 3 A flowchart of a machine learning model construction for a method, system and storage medium for checking enterprise financial data proposed by the present invention;

[0030] Figure 4 A data proofreading and verification flow chart of a method, system and storage medium for proofreading enterprise financial data proposed by the present invention;

[0031] Figure 5 This is a system optimization and upgrade flow chart of an enterprise financial data proofreading method, system and storage medium proposed by the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0034] Reference Figure 1-5 In a first aspect, the present invention provides a method for checking enterprise financial data, comprising the following steps:

[0035] S1: Data collection and preprocessing: Collect financial data from the company's financial system and perform preprocessing, including data cleaning, format conversion, data standardization, etc., to ensure data consistency and comparability;

[0036] More specifically, data collection: collecting financial data from the company's financial system, including financial statements, vouchers, account books, etc.; data cleaning: removing redundant information, invalid information and outliers in the data to ensure data consistency and comparability; format conversion: converting data in different formats into a unified format for subsequent processing and analysis; data standardization: standardizing the data, such as unifying the amount unit, date format, etc., to eliminate differences and conflicts between data.

[0037] S2: Feature extraction and selection: Extract features from preprocessed financial data, including numerical features, text features, time features, etc., and select key features based on the characteristics of the data and proofreading requirements;

[0038] More specifically, numerical feature extraction: extracting numerical features from financial data, such as amount, ratio, growth rate, etc., to analyze the quantitative relationship and change trend of data; text feature extraction: extracting text features from financial data, such as account name, summary information, etc., to analyze the semantic and logical relationship of data; time feature extraction: extracting time features from financial data, such as date, time interval, etc., to analyze the time distribution and change law of data; feature selection: selecting key features based on the characteristics of data and proofreading requirements, for building machine learning models. It should be noted that multi-dimensional features are extracted from financial data, including numerical features (such as amount, ratio, growth rate, etc.), text features (such as account name, summary information, etc.), time features (such as date, time interval, etc.), etc. These features can fully reflect the characteristics and laws of financial data and provide rich information for the construction of machine learning models.

[0039] S3: Machine learning model construction: Based on the extracted features, machine learning models are constructed, including classification models, regression models, clustering models, etc., to identify and process errors and anomalies in financial data;

[0040] More specifically, model selection: select appropriate machine learning models, such as classification models, regression models, clustering models, etc., according to the characteristics of the data and proofreading requirements; model training: use the extracted features and labeled data sets to train the model so that the model can learn the laws and characteristics of the data; model evaluation: evaluate the model through cross-validation, accuracy, recall and other indicators to ensure the performance and effect of the model. It should be noted that machine learning model construction uses machine learning algorithms, such as support vector machine (SVM), random forest, neural network, etc., to build intelligent proofreading models. These models can automatically learn the laws and characteristics of financial data based on historical data and feature information, thereby realizing intelligent identification and processing of data errors and anomalies. Compared with traditional fixed rules and algorithms, machine learning algorithms have stronger adaptability and learning ability, and can cope with complex and changing data errors and anomalies.

[0041] S4: Data proofreading and verification: Use the constructed machine learning model to proofread the financial data, including data consistency check, logical relationship verification, outlier detection, etc., and generate a proofreading report. At the same time, verify the proofreading results to ensure the accuracy and reliability of the proofreading;

[0042] More specifically, data consistency check: use machine learning models to check the consistency of financial data, such as checking whether debits and credits are balanced, whether the account balance is correct, etc.; logical relationship verification: use machine learning models to verify the logical relationship between financial data, such as checking whether the data in the income statement conforms to the logical relationship; outlier detection: use machine learning models to detect outliers in financial data, such as checking whether there are abnormally large amounts or abnormal proportions, etc.; generate proofreading reports: generate proofreading reports based on the proofreading results, including error lists, abnormal prompts, suggested measures, etc. It should be noted that the features of different dimensions are combined and integrated to improve the recognition ability and accuracy of the model, support real-time proofreading functions, and be able to proofread and verify in time as the financial data is updated and changed.

[0043] S5: Result output and feedback: Output the proofreading results to the user and provide a feedback mechanism to allow the user to confirm, modify or supplement the proofreading results to further improve the accuracy of the data;

[0044] More specifically, result output: output the proofreading results to the user, including error lists, exception prompts, etc.; user feedback: allow users to confirm, modify or supplement the proofreading results to further improve the accuracy of the data; result optimization: optimize and adjust the proofreading results according to user feedback and actual needs to improve the accuracy and reliability of the data. It should be noted that the system can dynamically adjust the proofreading rules and algorithms according to user feedback and actual needs to adapt to changes in different scenarios and needs. This real-time and dynamic nature ensures the timeliness and accuracy of financial data proofreading, and adopts a safe and reliable data processing mechanism, including data encryption, access control, data backup, etc., to ensure the security and confidentiality of financial data. At the same time, the system supports distributed storage and parallel processing of data, which improves the efficiency and reliability of data processing.

[0045] S6: System optimization and upgrade: Based on user feedback and actual needs, optimize and upgrade the system, including algorithm improvement, model update, function expansion, etc., to continuously improve the performance and effect of the system;

[0046] More specifically, algorithm improvement: based on user feedback and actual needs, improve and optimize the machine learning algorithm to improve the performance and effectiveness of the model; model update: regularly update the machine learning model to adapt to changes in different scenarios and needs; function expansion: based on the actual needs of users, expand the system's functions, such as adding data visualization, data analysis and other functions.

[0047] In the second aspect, the present invention provides an enterprise financial data proofreading system, including a user interface layer module: providing a user interaction interface for inputting proofreading requests, viewing proofreading results and providing user feedback; a business logic layer module: including modules such as data collection, preprocessing, feature extraction, machine learning model construction, data proofreading and result output, to realize intelligent proofreading of enterprise financial data; a data storage layer module: storing raw data, feature data, model data, proofreading results and other data to provide data support for the system.

[0048] More specifically, the corporate financial data proofreading process

[0049] Data collection: Collect financial data from the company's financial system.

[0050] Data preprocessing: Clean, format convert and standardize the collected data.

[0051] Feature extraction: extract numerical features, text features, time features, etc. from the preprocessed data.

[0052] Machine learning model construction: Build a machine learning model based on the extracted features.

[0053] Data proofreading: Use the constructed model to proofread financial data, including consistency checking, logical relationship verification, and outlier detection.

[0054] Result output: Output the proofreading results to the user.

[0055] User feedback: Allow users to confirm, modify or supplement the proofreading results.

[0056] System optimization: Optimize and upgrade the system based on user feedback and actual needs.

[0057] In a third aspect, the present invention provides a storage medium for checking corporate financial data, wherein the storage medium is burned with a computer program, and the computer program implements the above method when running in the memory of a server.

[0058] Machine Learning Model Building Process

[0059] Data preparation: Prepare data sets for model training, including labeled data and feature data.

[0060] Feature selection: Select key features based on the characteristics of the data and proofreading requirements.

[0061] Model selection: Choose an appropriate machine learning model.

[0062] Model training: Use labeled data and feature data to train the model.

[0063] Model evaluation: The model is evaluated through cross-validation, accuracy, recall and other indicators.

[0064] Model optimization: Optimize and adjust the model based on the evaluation results to improve the performance and effect of the model.

[0065] Data proofreading and verification process

[0066] Data consistency check: Use machine learning models to perform consistency checks on financial data, such as checking whether debits and credits are balanced and whether account balances are correct.

[0067] Logical relationship verification: Use machine learning models to verify the logical relationship between financial data, such as checking whether the data in the income statement conforms to the logical relationship.

[0068] Outlier detection: Use machine learning models to detect outliers in financial data, such as checking whether there are abnormally large amounts or abnormal proportions.

[0069] Generate proofreading report: Generate a proofreading report based on the proofreading results, including error list, exception prompts, suggested measures, etc.

[0070] The present invention can realize fast and accurate proofreading of financial data by adopting machine learning and big data analysis technology. Compared with traditional manual audit and simple computer program verification, the proofreading method of the present invention has higher efficiency and accuracy, can significantly improve the efficiency and quality of data proofreading, and can conveniently expand the functions and performance of the system by adopting modular and configurable design ideas. At the same time, the system supports real-time proofreading and dynamic adjustment functions, can be flexibly configured and adjusted according to different scenarios and needs to adapt to the development and changes of enterprise business, and can automatically identify and process errors and anomalies in financial data by adopting intelligent proofreading methods, reducing the cost and risk of manual intervention. At the same time, the system provides a user feedback mechanism, allowing users to confirm, modify or supplement the proofreading results, further improving the accuracy and reliability of the data. The implementation of the present invention can significantly improve the financial management level of the enterprise, including improving data quality, optimizing decision support, strengthening risk management and other aspects. By realizing fast and accurate proofreading of financial data, enterprises can better grasp their own financial status and operating conditions, and provide strong support for the decision-making and management of enterprises.

[0071] The above is a detailed introduction to a method, system and storage medium for checking corporate financial data provided by the present invention. Specific embodiments are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for checking enterprise financial data, characterized in that: The following steps are involved: S1: Data collection and preprocessing: Collect financial data from the company's financial system and perform preprocessing, including data cleaning, format conversion, data standardization, etc., to ensure data consistency and comparability; S2: Feature extraction and selection: Extract features from preprocessed financial data, including numerical features, text features, time features, etc., and select key features based on the characteristics of the data and proofreading requirements; S3: Machine learning model construction: Based on the extracted features, machine learning models are constructed, including classification models, regression models, clustering models, etc., to identify and process errors and anomalies in financial data; S4: Data proofreading and verification: Use the constructed machine learning model to proofread the financial data, including data consistency check, logical relationship verification, outlier detection, etc., and generate a proofreading report. At the same time, verify the proofreading results to ensure the accuracy and reliability of the proofreading; S5: Result output and feedback: Output the proofreading results to the user and provide a feedback mechanism to allow the user to confirm, modify or supplement the proofreading results to further improve the accuracy of the data; S6: System optimization and upgrade: Based on user feedback and actual needs, the system is optimized and upgraded, including algorithm improvement, model update, function expansion, etc., to continuously improve the performance and effect of the system.

2. A method for checking corporate financial data according to claim 1, characterized in that: S1: Data collection and preprocessing include the following steps: Data collection: Collect financial data from the company's financial system, including financial statements, vouchers, account books, etc.; Data cleaning: remove redundant information, invalid information and outliers in the data to ensure the consistency and comparability of the data; Format conversion: convert data in different formats into a unified format for subsequent processing and analysis; Data standardization: Standardize data, such as unifying the amount unit and date format, to eliminate differences and conflicts between data.

3. The enterprise financial data proofreading method according to claim 1, characterized in that: The feature extraction and selection of S2 includes the following steps; Numerical feature extraction: extracting numerical features from financial data, such as amount, ratio, growth rate, etc., to analyze the quantitative relationship and change trend of data; Text feature extraction: Extract text features from financial data, such as account names, summary information, etc., to analyze the semantics and logical relationships of the data; Time feature extraction: extract time features from financial data, such as date, time interval, etc., to analyze the time distribution and change patterns of data; Feature selection: Select key features based on the characteristics of the data and proofreading requirements for building machine learning models.

4. The method for checking enterprise financial data according to claim 1, characterized in that: The data proofreading and verification of S3 includes the following steps: Model selection: Select appropriate machine learning models, such as classification models, regression models, clustering models, etc., based on the characteristics of the data and proofreading requirements; Model training: Use the extracted features and annotated data sets to train the model so that the model can learn the laws and characteristics of the data; Model evaluation: S4 evaluates the model through cross-validation, accuracy, recall and other indicators to ensure the performance and effectiveness of the model.

5. The enterprise financial data proofreading method according to claim 1, characterized in that: The data checking and verification comprises the following steps: Data consistency check: Use machine learning models to check the consistency of financial data, such as checking whether debits and credits are balanced and whether the account balance is correct; Logical relationship verification: Use machine learning models to verify the logical relationship between financial data, such as checking whether the data in the income statement conforms to the logical relationship; Outlier detection: Use machine learning models to detect outliers in financial data, such as checking whether there are abnormally large amounts or abnormal proportions. Generate proofreading report: Generate a proofreading report based on the proofreading results, including error list, exception prompts, suggested measures, etc.

6. The enterprise financial data proofreading method according to claim 1, characterized in that: The S5 result output and feedback includes the following steps: Result output: Output the proofreading results to the user, including error list, exception prompts, etc.; User feedback: Allow users to confirm, modify or supplement the proofreading results to further improve the accuracy of the data; Result optimization: Based on user feedback and actual needs, the proofreading results are optimized and adjusted to improve the accuracy and reliability of the data.

7. The method for checking enterprise financial data according to claim 1, characterized in that: The S6 system optimization and upgrade includes the following steps: Algorithm improvement: Improve and optimize the machine learning algorithm based on user feedback and actual needs to improve the performance and effect of the model; Model update: Regularly update machine learning models to adapt to changes in different scenarios and needs; Functional expansion: Expand the system's functions based on actual user needs, such as adding data visualization, data analysis and other functions.

8. An enterprise financial data proofreading system, characterized in that: It includes a user interface layer module: it provides a user interaction interface for inputting proofreading requests, viewing proofreading results and providing user feedback; Business logic layer module: including modules such as data collection, preprocessing, feature extraction, machine learning model construction, data proofreading and result output, to realize intelligent proofreading of enterprise financial data; Data storage layer module: stores raw data, feature data, model data, proofreading results and other data to provide data support for the system.

9. A storage medium for checking corporate financial data, characterized in that: The storage medium is burned with a computer program, and the computer program implements the method described in any one of claims 1 to 7 when running in the memory of the server.

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