Financial data entry error correction system

Abstract

The invention relates to a financial data entry error correction system, which comprises a data preprocessing module used for clearing non-printing characters in input data; the data type identification module is used for defining a data mode; the real-time verification module is used for verifying whether the amount data is a numerical value; the error prompting and correction suggestion module is used for providing error prompting and correction suggestions when error data is found; a machine learning optimization module; the expandability and usability design is realized; and the user interaction interface supports a multi-language interface and provides real-time feedback and clear operation guidance. By implementing the financial data entry error correction system, the accuracy and efficiency of data entry can be remarkably improved by a financial institution, and format errors and data omission are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of financial credit, and in particular relates to a financial data entry error correction system. Background Art

[0002] In the financial credit business, data accuracy is crucial. However, due to human errors or limitations of the input system, problems such as format errors, data omissions, or unreasonable digital input often occur. These errors may lead to delays in the credit process, misjudgments in risk assessment, and even legal and compliance issues.

[0003] Most of the existing data input error correction systems have single functions, narrow applicability, or lack of real-time and accuracy, which makes it difficult to meet the financial market's demand for high-efficiency and high-quality data entry. Therefore, developing a financial data entry error correction system that can meet real-time, accuracy, ease of use and scalability is of great significance to improving credit business data processing. Summary of the invention

[0004] (I) Purpose of the invention

[0005] In order to overcome the above shortcomings, the purpose of the present invention is to provide a financial data entry error correction system to solve the above technical problems.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the technical solutions provided by this application are as follows:

[0008] A financial data entry error correction system, comprising:

[0009] Data preprocessing module, used to remove non-printing characters in input data, unify data format and encoding, standardize date and amount data formats, and remove unnecessary spaces before and after string data;

[0010] A data type identification module, which is used to define data patterns, apply machine learning algorithms to automatically infer data types, and use natural language processing technology to perform semantic analysis to assist in the identification of data types;

[0011] Real-time verification module, used to verify whether the amount data is a numeric value, check whether the value is within a reasonable range, verify whether there are illegal characters in the amount, verify whether the date format conforms to the predefined format, check whether the date logic is valid, and verify whether other data such as ID card and email address formats are correct;

[0012] The error prompt and correction suggestion module is used to provide error prompts and correction suggestions when erroneous data is found, including a quick feedback mechanism and suggestions for error correction;

[0013] A machine learning optimization module that collects training data from user modifications and feedback, analyzes the correlation between error data and user modification data, identifies error patterns, and regularly updates the model with new training data;

[0014] Scalability and ease of use design, including custom rule configuration and API integration to adapt to the specific needs of different financial institutions;

[0015] The user interaction interface supports multi-language interface and provides real-time feedback and clear operation instructions.

[0016] Preferably, the data processing module further includes:

[0017] Data standardization: remove non-printing characters from all input data, unify them into simplified or traditional Chinese, delete extra spaces, and convert data into a unified format and encoding;

[0018] Normalize data, standardize the date data format to "YYYY-MM-DD", standardize the amount format, and remove unnecessary spaces before and after the input string data.

[0019] Preferably, the data type identification module further includes:

[0020] Use regular expressions to define typical formats for data;

[0021] Automatically infer data types using classification algorithms;

[0022] Use NLP technology to perform semantic analysis on the input text data.

[0023] Preferably, the real-time verification module further includes:

[0024] Verify the amount data to check whether the amount is a numeric value, whether the numeric value is within the reasonable range specified by the business, and whether there are illegal characters in the amount;

[0025] Validate date data to verify whether the date format conforms to the predefined format and check whether the date logic is valid;

[0026] For other data verification, verify whether the ID card meets national standards and logical tests, and check whether the email or other professional ID format is correct.

[0027] Preferably, the error prompt and correction suggestion module also includes:

[0028] Provides the exact location of the error, including highlighting the error field in the user interface;

[0029] Provide suggestions for error correction, including guidance on the correct format that users should enter;

[0030] Provide autocorrect options to let users choose whether to accept autocorrections.

[0031] Preferably, the machine learning optimization module further includes:

[0032] Collect training data from user modifications and feedback;

[0033] Data storage and management: the collected training data is stored in a dedicated database and classified by data type, error type, business area or other categories;

[0034] Data cleaning and preprocessing: clean and preprocess the collected training data to remove noise data and outliers;

[0035] Analyze the correlation between error data and user modification data, identify error patterns, and conduct user behavior analysis.

[0036] Preferably, the scalability and usability design also includes:

[0037] Allow users to intuitively set and modify validation rules on the interface;

[0038] Provide API calls and configuration files to support automated rule customization;

[0039] Provides a rich set of API interfaces for other systems to access and expand error checking capabilities.

[0040] Preferably, the user interaction interface further includes:

[0041] Support multi-language interface to meet the needs of different users;

[0042] Provides intuitive operation interfaces such as menus and toolbars, provides a friendly user interaction interface, and supports multiple input methods;

[0043] Provide real-time feedback on the data entry interface, marking errors and prompts;

[0044] Provide clear operation instructions and prompt information to guide users to enter data correctly.

[0045] Beneficial effects:

[0046] 1. Improve the accuracy and efficiency of financial data entry and reduce the problems of format errors and data omissions.

[0047] 2. The use of advanced data identification and verification technology has improved the intelligence level and processing speed of the system.

[0048] 3. Through real-time error prompts and correction suggestions, it helps users quickly correct errors and optimizes the user experience.

[0049] 4. Through machine learning optimization technology, the dependence on human intervention is reduced and the stability of the system is improved.

[0050] 5. Good scalability and ease of use design enable the system to flexibly adapt to the specific needs of different financial institutions.

[0051] 6. For financial institutions, it can significantly reduce delays, risk assessment errors and compliance issues caused by erroneous data.

[0052] In general, the financial data entry error correction system will provide an efficient, intelligent and easy-to-use data entry auxiliary tool for financial credit business, helping financial institutions to improve the accuracy, stability and user experience of data processing, thereby improving the overall quality and efficiency of financial credit business. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following is a detailed description of the present invention in conjunction with the specific implementation methods and with reference to the attached drawings. Figure 1 , the present invention is further described in detail. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0055] A financial data entry error correction system, comprising:

[0056] Data preprocessing module, used to remove non-printing characters in input data, unify data format and encoding, standardize date and amount data formats, and remove unnecessary spaces before and after string data;

[0057] A data type identification module, which is used to define data patterns, apply machine learning algorithms to automatically infer data types, and use natural language processing technology to perform semantic analysis to assist in the identification of data types;

[0058] Real-time verification module, used to verify whether the amount data is a numeric value, check whether the value is within a reasonable range, verify whether there are illegal characters in the amount, verify whether the date format conforms to the predefined format, check whether the date logic is valid, and verify whether other data such as ID card and email address formats are correct;

[0059] The error prompt and correction suggestion module is used to provide error prompts and correction suggestions when erroneous data is found, including a quick feedback mechanism and suggestions for error correction;

[0060] A machine learning optimization module that collects training data from user modifications and feedback, analyzes the correlation between error data and user modification data, identifies error patterns, and regularly updates the model with new training data;

[0061] Scalability and ease of use design, including custom rule configuration and API integration to adapt to the specific needs of different financial institutions;

[0062] The user interaction interface supports multi-language interface and provides real-time feedback and clear operation instructions.

[0063] The data processing module also includes:

[0064] Data standardization: remove non-printing characters from all input data, unify them into simplified or traditional Chinese, delete extra spaces, and convert data into a unified format and encoding;

[0065] Normalize data, standardize the date data format to "YYYY-MM-DD", standardize the amount format, for example, unify "$1,000.00" to "1000.00", and remove unnecessary spaces before and after the input string data.

[0066] The data type identification module also includes:

[0067] Use regular expressions to define the typical format of data. For example, for telephone numbers, define the pattern as + plus or minus country code, number;

[0068] Defines multiple confusing formats for dates and amounts;

[0069] Automatically infer data types using classification algorithms;

[0070] Train the model to distinguish between data types such as numbers, text, amounts, and dates;

[0071] Use NLP technology to perform semantic analysis on the input text data.

[0072] The real-time verification module also includes:

[0073] Verify the amount data to check whether the amount is a numeric value, whether the numeric value is within the reasonable range specified by the business, and whether there are illegal characters in the amount;

[0074] Validate date data to verify whether the date format conforms to the predefined format and check whether the date logic is valid, including dates that do not exist in the future or unreasonable date spans;

[0075] For other data verification, verify whether the ID card meets national standards and logical tests, and check whether the email or other professional ID format is correct.

[0076] The error prompt and correction suggestion module also includes:

[0077] When an error is encountered in verifying data, feedback is immediately given to the user;

[0078] Provides the exact location of the error, including highlighting the error field in the user interface;

[0079] Provide suggestions for error correction, including guidance on the correct format that users should enter;

[0080] Provide autocorrect options to let users choose whether to accept autocorrections.

[0081] The machine learning optimization module also includes:

[0082] Collect training data from user modifications and feedback;

[0083] Data collection mechanism: Whenever a user makes a modification in the financial data entry system, the system automatically records the original erroneous data and the corrected data after modification. At the same time, when a user provides feedback, such as pointing out the cause of the error in specific data or making suggestions for improvement, this feedback is also associated with the relevant data and recorded.

[0084] Data storage and management: The collected training data is stored in a dedicated database and classified by data type, error type, business field, etc. for subsequent analysis and use. For example, the data can be divided into different categories such as transaction data and customer information data, and separate analysis and model training can be performed for different categories of data.

[0085] Data cleaning and preprocessing: Clean and preprocess the collected training data to remove noise data and outliers. For example, filter and correct obviously unreasonable data values, such as negative asset amounts and interest rates that exceed reasonable ranges. At the same time, standardize the data so that different types of data have a unified format and scale to facilitate model training and application.

[0086] Analyze the correlation between error data and user-modified data.

[0087] Association analysis method: Use data mining and machine learning algorithms to analyze the association between error data and user-modified data. For example, you can use association rule mining algorithms to find combinations of data that often have errors and are modified at the same time; or use clustering algorithms to cluster data with similar error patterns and modification methods into one category to better understand the types and patterns of errors.

[0088] Error pattern identification: Common error patterns can be identified through analysis of correlations. For example, it may be found that certain data fields are prone to input errors, such as incorrect numeric input and incorrect date formats, or certain types of errors are prone to occur in certain business scenarios, including data calculation errors in financial statements and amount matching errors in transaction data. These error patterns can provide important references for subsequent model optimization and verification logic.

[0089] User behavior analysis: In addition to analyzing the correlation between data, you can also analyze the user's modification behavior. For example, you can understand the modification methods, modification speed, and frequency that users usually take when they find errors. This information can help optimize the system's user interface and interaction design, improve user experience, and error correction efficiency.

[0090] Model optimization:

[0091] Regularly update the model with new training data.

[0092] Use the generated model to optimize the validation logic and improve accuracy.

[0093] The scalability and ease of use design also includes:

[0094] Allow users to intuitively set and modify validation rules on the interface;

[0095] Provide API calls and configuration files to support automated rule customization;

[0096] Provides a rich set of API interfaces for other systems to access and expand error checking capabilities;

[0097] Ensure the security and stability of data transmission.

[0098] The user interaction interface also includes:

[0099] Support multi-language interface to meet the needs of different users;

[0100] Provides an intuitive operation interface of menus and toolbars, a friendly user interaction interface, and supports multiple input methods, including text boxes and drop-down menus;

[0101] Provide real-time feedback on the data entry interface, marking errors and prompts;

[0102] Provide clear operation instructions and prompt information to guide users to enter data correctly.

[0103] The key points of the present invention include: real-time data verification technology: it realizes real-time verification and error detection of different types of data; data type intelligent identification technology: it adopts advanced natural language processing and machine learning technology to improve the accuracy and efficiency of data identification; error prompt and correction suggestion mechanism: it provides real-time and accurate error prompts and correction suggestions when errors are found, helping users to correct errors quickly; machine learning optimization technology: the system can learn from historical error data and continuously optimize the accuracy and efficiency of verification rules and error prompts; scalability and ease of use design: it supports custom data verification rules and error prompts, and provides a friendly user interaction interface and API interface to meet the needs of different financial institutions.

[0104] By implementing the financial data entry error correction system, the present invention enables financial institutions to significantly improve the accuracy and efficiency of data entry, reduce format errors and data omissions, and use advanced data identification and verification technologies to enhance the intelligence and processing speed of the system. The real-time error prompts and correction suggestions provided by the system optimize the user experience, while the machine learning optimization technology reduces manual intervention and enhances the stability of the system. In addition, the scalability and ease of use of the system enable it to adapt to the specific needs of different financial institutions, thereby reducing delays, risk assessment errors and compliance issues caused by erroneous data, and comprehensively improving the data processing quality, stability and user experience of financial credit business.

[0105] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A financial data entry error correction system, characterized in that: include: Data preprocessing module, used to remove non-printing characters in input data, unify data format and encoding, standardize date and amount data formats, and remove unnecessary spaces before and after string data; A data type identification module, which is used to define data patterns, apply machine learning algorithms to automatically infer data types, and use natural language processing technology to perform semantic analysis to assist in the identification of data types; Real-time verification module, used to verify whether the amount data is a numeric value, check whether the value is within a reasonable range, verify whether there are illegal characters in the amount, verify whether the date format conforms to the predefined format, check whether the date logic is valid, and verify whether other data such as ID card and email address formats are correct; The error prompt and correction suggestion module is used to provide error prompts and correction suggestions when erroneous data is found, including a quick feedback mechanism and suggestions for error correction; A machine learning optimization module that collects training data from user modifications and feedback, analyzes the correlation between error data and user modification data, identifies error patterns, and regularly updates the model with new training data; Scalability and ease of use design, including custom rule configuration and API integration to adapt to the specific needs of different financial institutions; The user interaction interface supports multi-language interface and provides real-time feedback and clear operation instructions.

2. According to claim 1, a financial data entry system is characterized in that: The data processing module also includes: Data standardization: remove non-printing characters from all input data, unify them into simplified or traditional Chinese, delete extra spaces, and convert data into a unified format and encoding; Normalize data, standardize the date data format to "YYYY-MM-DD", standardize the amount format, and remove unnecessary spaces before and after the input string data.

3. A financial data entry error correction system according to claim 1, characterized in that: The data type identification module also includes: Use regular expressions to define typical formats for data; Automatically infer data types using classification algorithms; Use NLP technology to perform semantic analysis on the input text data.

4. A financial data entry error correction system according to claim 1, characterized in that: The real-time verification module also includes: Verify the amount data to check whether the amount is a numeric value, whether the numeric value is within the reasonable range specified by the business, and whether there are illegal characters in the amount; Validate date data to verify whether the date format conforms to the predefined format and check whether the date logic is valid; For other data verification, verify whether the ID card meets national standards and logical tests, and check whether the email or other professional ID format is correct.

5. A financial data entry error correction system according to claim 1, characterized in that: The error prompt and correction suggestion module also includes: Provides the exact location of the error, including highlighting the error field in the user interface; Provide suggestions for error correction, including guidance on the correct format that users should enter; Provide autocorrect options to let users choose whether to accept autocorrections.

6. A financial data entry error correction system according to claim 1, characterized in that: The machine learning optimization module also includes: Collect training data from user modifications and feedback; Data storage and management: the collected training data is stored in a dedicated database and classified by data type, error type, business area or other categories; Data cleaning and preprocessing: clean and preprocess the collected training data to remove noise data and outliers; Analyze the correlation between error data and user modification data, identify error patterns, and conduct user behavior analysis.

7. A financial data entry error correction system according to claim 1, characterized in that: The scalability and ease of use design also includes: Allow users to intuitively set and modify validation rules on the interface; Provide API calls and configuration files to support automated rule customization; Provides a rich set of API interfaces for other systems to access and expand error checking capabilities.

8. A financial data entry error correction system according to claim 1, characterized in that: The user interaction interface also includes: Support multi-language interface to meet the needs of different users; Provides intuitive operation interfaces such as menus and toolbars, provides a friendly user interaction interface, and supports multiple input methods; Provide real-time feedback on the data entry interface, marking errors and prompts; Provide clear operation instructions and prompt information to guide users to enter data correctly.

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