Invoice Receipt Matching via Line-Level Distance Algorithms
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Solution Overview
Problem
Existing invoice management systems face challenges in accurately matching invoices with receipts due to large data volumes, incomplete or incorrect data, and the complexity of multi-party communications, leading to high error rates and operational overheads, including false claims and disputes.
Innovation Solution
A method and system using line-level matching based on distance algorithms and machine learning modeling to identify matches and calculate deviations between invoices and receipts, incorporating user feedback for retraining to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional matching approaches are used to match invoices with receipts, then the system can process invoices, but the matching accuracy is low and false claims/disputes are generated
Solution Approach 1:
The patent applies segmentation by breaking down the invoice and receipt data into line-level attributes (item descriptions, quantities, prices, etc.) and matching them individually rather than treating the entire documents as single units. This granular approach enables more precise comparison and reduces false matches by identifying specific discrepancies at the line item level.
Solution Approach 2:
The patent transforms the matching process by changing from traditional rule-based parameter comparison to distance-based algorithms that calculate similarity scores across multiple parameters (text similarity, numerical differences, temporal relationships). This parameter transformation enables nuanced matching that accounts for variations in data formatting and minor discrepancies while maintaining high accuracy.
2Reliability
If manual review processes are used to verify invoice-receipt matches, then false claims can be reduced, but operational overhead and processing time increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform invoice-receipt matching and dispute identification without requiring manual intervention. The distance-based algorithms and machine learning models autonomously analyze data, calculate matches, and flag potential disputes, allowing the system to serve itself in the matching process while maintaining high reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from matched and disputed invoice-receipt pairs. User corrections and verified matches are fed back into the model to refine distance calculations and improve future matching accuracy, creating a self-improving system that reduces operational overhead while maintaining or improving claim accuracy over time.
3Loss of information
If complex multi-party communication data is processed to improve matching, then more context is available, but data complexity and processing difficulty increase
Solution Approach 1:
The patent applies the extraction principle by isolating and focusing on the most relevant line-level attributes from complex multi-party communication data. Rather than processing all available data, the system extracts key matching criteria (item descriptions, quantities, prices, dates) from invoices, receipts, and related communications, filtering out unnecessary complexity while preserving essential information for accurate matching.
4Measurement precision
If distance-based algorithms with machine learning are used for line-level matching, then matching accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by implementing a two-stage matching process: first using faster, simpler distance-based algorithms for initial screening and coarse matching, then applying more computationally intensive machine learning models only to borderline cases or high-value invoices. This selective approach achieves high overall accuracy while minimizing unnecessary computational resource consumption on clearly matching or clearly non-matching items.
Data Source
AI summary
A method and system for detecting deviation between invoices and receipts are disclosed. In some embodiments, the method includes receiving invoice data and receipt data. The method includes filtering the received data to generate filtered data. The method includes performing line-level matching on the filtered data based on one or more line-level attributes and one or more distance based algorithms. The method then includes determining, from the line-level matching, matched line items and unmatched line items between each pair of the invoice and receipts. The method also includes calculating one or more types of claims for both the matched line items and the unmatched line items to measure a total deviation between the invoices and receipts. The method further includes determining a level of match between the invoices and receipts and generating a recommended matching pair of invoice and receipt based on the level of match. The matches are further improved by user feedback to the recommended pairs which is used to train a machine learning model.