Augmented OCR for Financial Documents Using Business Rules
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Solution Overview
Problem
Traditional tax return preparation systems and other financial systems face challenges in providing accurate and efficient optical character recognition (OCR) analysis, leading to errors that can result in significant consequences for users, such as incorrect tax payments and penalties, due to the high resource requirements for processing and storing large volumes of data.
Innovation Solution
A method and system that generates augmented OCR data by analyzing both image data from financial documents and relevant user-related financial data, using historical and finance law data to detect errors, fill vacant fields, and provide notifications, thereby improving accuracy without requiring excessive processing and storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional OCR analysis is performed on large volumes of financial documents, then data import automation is achieved, but processing and storage resource requirements become excessively large
Solution Approach 1:
The patent segments the OCR processing task by dividing financial documents into individual fields (e.g., name, address, income) and processing each field separately with targeted validation rules. This segmentation allows the system to process only relevant portions of documents rather than analyzing entire documents uniformly, reducing overall processing and storage resource requirements while maintaining automation.
Solution Approach 2:
The patent applies local quality by implementing field-specific validation rules and data type checks tailored to each OCR-extracted field. Different validation strategies are applied to different fields (e.g., numerical validation for income fields, format validation for address fields), optimizing processing efficiency for each local context rather than applying a single resource-intensive universal processing approach.
2Extent of automation
If traditional OCR analysis is performed on financial documents, then automatic data import is achieved, but data accuracy deteriorates due to errors in recognition
Solution Approach 1:
The patent implements feedback mechanisms where OCR-extracted data is validated against predefined business rules, data type constraints, and cross-field consistency checks. When validation failures occur, the system provides feedback to identify specific errors and allows for corrective action, thereby maintaining high automation levels while improving data accuracy through iterative validation and correction.
Solution Approach 2:
The patent applies preliminary action by performing validation checks and error detection on OCR-extracted data before the data is committed to the financial system. Business rules and constraints are applied in advance to catch potential errors early in the process, preventing inaccurate data from propagating through the system and requiring later correction.
3Speed
If OCR analysis is performed without validation, then processing speed is maintained, but data reliability deteriorates leading to incorrect tax payments and penalties
Solution Approach 1:
The patent applies partial action by implementing selective validation rules that focus on the most critical fields and error-prone data types. Rather than performing exhaustive validation on every single data point, the system applies targeted validation to key fields (e.g., social security numbers, income amounts) that have the greatest impact on tax calculation accuracy, maintaining processing speed while improving reliability for critical data.
Data Source
AI summary
A method and system provides augmented OCR data to a user of a financial system. The method and system include receiving image data related to an image of a financial document of the user and generating OCR data based on the image data. The method and system further include receiving financial data related to the financial document, analyzing the financial document, and generating the augmented OCR data based on the OCR data and the financial data.