Automated Transaction Document Auditing With OCR Verification
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Solution Overview
Problem
Auditing of transaction documents, such as those related to vehicle purchases and mortgage transactions, is often manual and time-consuming, leading to errors due to incomplete or inaccurate data entry, and only a small portion of documents are thoroughly reviewed, resulting in negative ramifications.
Innovation Solution
A computer-implemented method and system that receives a transaction package, separates documents into individual pages, classifies them by type, extracts text using trained algorithms, generates structured data by normalizing across pages, and provides a user interface for visual status and auditing, including structured auditing questions to verify data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual auditing of transaction documents is performed, then auditing accuracy can be maintained through human review, but auditing efficiency and productivity are severely limited
Solution Approach 1:
The patent replaces manual mechanical auditing processes with an automated computer-based system that uses optical character recognition (OCR), machine learning classification, and automated verification algorithms to extract, classify, and validate transaction document data, dramatically improving productivity while reducing time consumption
Solution Approach 2:
The system enables self-service auditing by automatically processing transaction documents through intelligent algorithms that classify documents by type, extract relevant data fields, and perform verification without requiring manual human intervention for each document review
2Reliability
If only a small portion of transaction documents are manually audited, then resource consumption is reduced, but auditing reliability and error detection capability deteriorate
Solution Approach 1:
The patent implements a universal auditing system that can handle multiple types of transaction documents (real estate, vehicle, personal property) through a single platform with document-type-specific classification algorithms, achieving high reliability across diverse document types without proportionally increasing system complexity
Solution Approach 2:
The system segments the auditing process into distinct automated stages: document classification by type, data extraction using OCR and machine learning, normalization of extracted data, and verification against predefined criteria. This segmentation allows comprehensive auditing of all documents while managing complexity through modular processing
3Measurement precision
If manual data entry is performed for transaction documents, then data accuracy can be verified through human review, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual data entry and verification with automated optical character recognition (OCR) technology and machine learning-based extraction algorithms that rapidly convert document images into structured data with high accuracy, eliminating human error while maintaining precision through algorithmic validation
Solution Approach 2:
The system implements feedback mechanisms where extracted data is automatically verified against expected formats, cross-referenced with source documents, and validated through consistency checks. Errors or inconsistencies trigger alerts for review, ensuring high data extraction accuracy while maintaining rapid automated processing
Data Source
AI summary
An auditing system may process documents associated with a transaction to audit the entire transaction or the documents involved in the transaction. The documents are received and classified by document types. Structured text, unstructured text or both are extracted from the documents and structured data is produced using the extracted text. The documents are audited using the structured data. In some embodiments, the documents may be audited using structured auditing questions, automated programmatic verification, external data sources, or combinations thereof.