Automated Remittance Data Mapping with OCR and Machine Learning
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Solution Overview
Problem
Current software solutions for data mapping in accounts receivables departments require manual efforts to cross-reference remittance document numbers with accounts receivable data, leading to time-intensity and susceptibility to errors, which can cause misallocations and discrepancies in financial records.
Innovation Solution
A machine learning-based computing system and method that utilizes optical character recognition (OCR) and a machine learning model, such as a random forest model, to automatically extract and map data from electronic documents, correlating information with metadata to determine linkage values and update databases, reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual cross-referencing is used to map remittance document numbers with accounts receivable data, then data mapping can be performed, but the process becomes time-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical cross-referencing with an automated machine learning system that uses optical character recognition (OCR) to extract document numbers and a trained model to match them with accounts receivable data, eliminating the need for manual intervention while improving accuracy and reducing time
Solution Approach 2:
The system enables self-service automation where the machine learning model independently performs the entire mapping process from document ingestion to data association without human intervention, allowing the system to serve itself in completing the reconciliation workflow
2Productivity
If manual mapping is performed by collection personnel, then payments can be allocated to invoices, but human operators may overlook or misinterpret vital information leading to errors
Solution Approach 1:
The patent replaces human operators with an automated machine learning system that objectively extracts and compares document numbers without fatigue, distraction, or misinterpretation, ensuring consistent and accurate payment allocation while maintaining high productivity
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from mapping results, improving its accuracy over time by analyzing successful matches and correcting errors, thereby enhancing reliability while maintaining productivity
3Ease of operation
If current software solutions are used, then the workflow can proceed, but autonomous association of document numbers with accounts receivable data is not achieved
Solution Approach 1:
The system achieves full autonomy by enabling the machine learning model to independently extract document numbers, search accounts receivable data, perform matching, and complete payment allocation without any manual intervention, thereby maximizing automation while maintaining ease of operation through automated workflows
Data Source
AI summary
A machine learning based computing method for automatic data mapping for electronic documents (e.g., remittance documents or documents including remittance information) is disclosed. The machine learning based computing method includes: receiving electronic documents from first databases; extracting data comprising first information from the electronic documents based on OCR conversion process; determining first linkage values for the first information by correlating the first information with metadata extracted from the first information based on a machine learning model; mapping second linkage values with linkage keys in second databases; mapping first pairs associated with the second linkage values and the linkage keys, with second pairs associated with end-state values and end-state keys in the second databases; updating the second databases by adjusting payments associated with the first information; and providing an output of the second databases with the adjusted payments to first users on a user interface associated with electronic devices.