Machine Learning Data Extraction from Financial Statements
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Solution Overview
Problem
Current methods for extracting data from financial documents, such as balance sheets, income statements, and cash flow statements, face challenges due to lack of standardization, low accuracy, and inefficiency in adapting to different document formats, requiring manual intervention and reconfiguration for each new type.
Innovation Solution
A machine learning-based computing system using convolutional neural networks (CNN) and You Only Look Once (YOLO) architecture to classify and extract data from financial documents, converting formats into images, identifying tabular and non-tabular data, and employing optical character recognition (OCR) for accurate data extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If generic document parsing software and OCR systems are used to process financial statements, then data extraction can be automated, but accuracy and processing speed remain low
Solution Approach 1:
The patent transforms the document processing approach by converting documents into image formats and applying deep learning models (CNN for classification, YOLO for detection) instead of traditional OCR. This parameter change in the processing methodology enables the system to achieve high accuracy (95%+) in extracting financial data from standardized documents while maintaining full automation.
Solution Approach 2:
The patent replaces traditional mechanical OCR systems with neural network-based image recognition systems. The CNN-YOLO pipeline substitutes the conventional character recognition mechanism with deep learning-based detection and classification, achieving superior accuracy in extracting tabular financial data while preserving automation.
2Measurement precision
If parsing systems are customized for specific clients or document formats, then processing accuracy for those formats improves, but adaptability to handle financial documents from different clients decreases
Solution Approach 1:
The patent creates a universal processing pipeline that can handle multiple document formats (PDF, Excel, Word) through a common image conversion and deep learning-based extraction approach. The standardized processing methodology enables the system to adapt to different clients' document formats without requiring customization, while maintaining high extraction accuracy across diverse financial statements.
3Measurement precision
If manual input by financial analysts is used, then data extraction accuracy improves, but processing time and labor costs increase
Solution Approach 1:
The patent implements a self-service automated extraction system that processes financial documents without human intervention. The CNN-YOLO pipeline automatically converts documents to images, classifies them, detects tabular regions, and extracts financial data with high accuracy, eliminating the need for manual analyst input while achieving processing speeds that handle large volumes of documents efficiently.
4Measurement precision
If reconfiguration of parsing systems is performed for new document types, then processing accuracy for new formats improves, but time consumption and efficiency decrease
Solution Approach 1:
The patent performs preliminary conversion of all document formats into a standardized image format, which serves as a universal intermediate representation. This preliminary action eliminates the need for format-specific configuration when processing new document types, as the image-based processing pipeline remains consistent across all formats, reducing reconfiguration time to minimal steps.
Data Source
AI summary
A machine learning based computing method for extracting data from electronic documents, is disclosed. The machine learning based computing method includes steps of: receiving the electronic documents from data sources; converting formats of the electronic documents into first images associated with the electronic documents; classifying the first images associated with the electronic documents as second images including tabular data, and third images including non-tabular data, based on a CNN based machine learning model; determining tabular regions in tabular data associated with the second images by identifying bounding box coordinates indicating tabular regions in tabular data, based on a YOLO architecture based machine learning model; extracting first data from the tabular regions; extracting second data from non-tabular regions; and providing an output of the first data and the second data to second users on a user interface associated with electronic devices.