ML Document Processing for Financial Data Extraction
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Solution Overview
Problem
The processing of financial documents, such as invoices and claims, is hindered by the lack of industry-standard layouts or formats, leading to inefficient manual processing and errors, despite the use of general-purpose document parsing software and Optical Character Recognition (OCR) systems.
Innovation Solution
A machine learning-based system that automates the parsing and extraction of data from financial documents by accessing data files, extracting information, classifying it using a trained machine learning model, and generating tabular and key-value pair data, with continuous model updates for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If general-purpose document parsing software and OCR systems are used, then document processing can be automated, but processing accuracy and adaptability to diverse formats deteriorate
Solution Approach 1:
The patent transforms fixed template-based parameters into dynamic machine learning model parameters that adapt to different document formats. The system uses trained ML models to automatically adjust extraction parameters based on document type, layout variations, and content patterns, thereby maintaining high accuracy across diverse formats without requiring manual template configuration for each document type.
Solution Approach 2:
The system implements self-service through automated ML model training and selection. The platform automatically trains models on uploaded documents, selects appropriate models based on document characteristics, and continuously improves extraction accuracy without human intervention. This eliminates the need for manual template creation and configuration while maintaining high processing accuracy.
2Measurement precision
If manual processing by finance teams is used, then processing accuracy can be maintained, but processing speed and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical processing by finance teams with an automated machine learning-based system. The ML models perform data extraction, validation, and population of accounting systems automatically, achieving both high accuracy through learned patterns and high speed through parallel processing capabilities, thereby eliminating the speed-accuracy trade-off inherent in manual processing.
3Measurement precision
If template-specific configurations are developed for each document format, then processing accuracy improves, but system complexity and development time deteriorate
Solution Approach 1:
The patent creates a universal machine learning platform that handles multiple document formats through a single system architecture. Instead of developing separate templates for each format, the system uses trained ML models that can process invoices, claims, remittances, and other financial documents with varying layouts and formats, thereby reducing system complexity while maintaining format-specific accuracy.
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on diverse document formats before actual processing. This advance training prepares the models to automatically adapt to different formats without requiring runtime configuration or template selection, thereby simplifying the system architecture while maintaining high accuracy across formats.
4Reliability
If manual processing is used for extensive datasets, then data quality can be monitored, but resource consumption and cost deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning system automatically monitors data extraction quality, validates extracted information against business rules, and adjusts processing parameters based on detected errors or anomalies. This automated feedback loop maintains data quality monitoring while eliminating the need for manual review, thereby reducing resource consumption and costs associated with extensive manual processing of large datasets.
Data Source
AI summary
The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.