OCR Receipt Reconciliation with ML Discrepancy Detection
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Solution Overview
Problem
Reconciling physical records with electronic records is often a laborious process, requiring manual comparisons to identify discrepancies, which can lead to errors and fraud going undetected.
Innovation Solution
A computer-based method that uses optical character recognition and machine learning to encode transaction information from digital images of receipts, matching it with transaction histories to automatically identify discrepancies and alert users, allowing for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison methods are used to reconcile physical records with electronic records, then the process can identify discrepancies, but the process is laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical comparison processes with an automated optical character recognition (OCR) system. The OCR technology captures images of physical receipts, automatically extracts transaction data, and compares it with electronic records through computer algorithms, eliminating the need for manual line-by-line verification while maintaining high accuracy in discrepancy detection.
Solution Approach 2:
The system enables self-service automation where the reconciliation process performs itself without human intervention. The OCR system automatically captures receipt images, extracts data, matches transactions with electronic records, and generates discrepancy reports autonomously, freeing employees from manual reconciliation tasks while improving both speed and accuracy.
2Adaptability or versatility
If manual reconciliation processes are used, then flexibility in handling various record formats is maintained, but error detection and fraud identification become less reliable
Solution Approach 1:
The OCR system dynamically adjusts extraction parameters and recognition algorithms based on the specific format of each receipt being processed. It can handle various layouts, fonts, and data structures by modifying its processing parameters in real-time, maintaining flexibility across different record formats while ensuring consistent and reliable data extraction for accurate discrepancy detection.
3Productivity
If automated OCR systems are implemented, then reconciliation speed and accuracy improve, but system complexity increases
Solution Approach 1:
The patent divides the complex reconciliation system into distinct functional modules: an OCR module for image processing and data extraction, a data validation module for verifying extracted information, a matching module for comparing physical and electronic records, and a reporting module for generating discrepancy reports. This segmentation manages system complexity by making each component independent and specialized, while collectively achieving high productivity.
Data Source
AI summary
Systems and methods for detecting and mitigating fraud include a processor for performing steps including receiving a digital image of a receipt and utilizing an optical character recognition model to encode a digital representation of transaction information from the receipt. The processor extracts a payee feature, an amount feature, and a payment date feature from the transaction data, and generating a receipt feature vector from the payee feature, the amount feature and the payment date feature. The processor receives historical transaction data representing historical transactions, generates a transaction feature vector for each historical transaction and then utilizes a machine learning model to predict a matching transaction from the transaction history that matches the receipt based on the receipt feature vector and each of the transaction feature vectors to determine a difference between the payment amount of the receipt and the payment authorization of the matching transaction.