AI Table Extraction From Scanned Financial Statements
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Solution Overview
Problem
Traditional systems and methods struggle to accurately identify and extract key financial information from non-standard formatted financial statements, leading to manual, time-consuming, and error-prone processes in financial spreading.
Innovation Solution
A computer-implemented system using an object detection algorithm and optical character recognition to identify tables in documents, extract text within these tables, and map the data to structured formats, utilizing techniques like sliding window and natural language processing to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used for financial spreading, then accuracy can be maintained through human judgment, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces the mechanical manual process of financial spreading with an automated computer vision system. The system uses document scanning, optical character recognition (OCR), and machine learning algorithms to automatically extract financial data from statements, replacing the manual work of credit analysts while maintaining high accuracy through trained models and validation processes.
Solution Approach 2:
The system enables self-service automation where the financial spreading process performs itself without human intervention. The automated system scans documents, identifies tables, extracts data, maps to structured formats, and validates results independently, freeing analysts from repetitive manual tasks while maintaining quality through built-in validation rules.
2Productivity
If automated systems are implemented for data extraction, then productivity increases, but the system must handle non-standard formats and varying document structures
Solution Approach 1:
The patent creates a universal automated system capable of handling multiple document formats and structures. The system uses format-agnostic document scanning, flexible table detection algorithms, and adaptable data mapping that can accommodate various financial statement layouts from different companies and jurisdictions, making it versatile enough to process diverse documents at high speed.
Solution Approach 2:
The system dynamically adjusts processing parameters based on document characteristics. It detects document type, table structures, and data formats, then modifies extraction and mapping parameters accordingly. This allows the system to maintain high productivity across varying document formats by adapting its processing approach to each specific document's structure.
3Measurement precision
If manual review of financial documents is performed, then relevant sections can be identified accurately, but the process is costly and resource-intensive
Solution Approach 1:
The patent segments the complex document review process into distinct automated stages: document scanning, table detection, data extraction, structure identification, and validation. Each segment is handled by specialized algorithms working in sequence, replacing manual review while maintaining accuracy through focused, automated analysis of each document component.
4Ease of operation
If existing systems are used for data extraction, then basic functionality is provided, but they cannot accurately identify relevant tables or map data to structured formats
Solution Approach 1:
The patent introduces an intermediary layer of intelligent processing between document input and data output. This includes table detection intermediaries that identify relevant financial tables, OCR intermediaries that accurately transcribe data, and mapping intermediaries that transform unstructured data into structured formats. These intermediaries enhance extraction accuracy while maintaining operational simplicity through automated workflows.
Data Source
AI summary
Consistent with disclosed embodiments, systems, devices, and methods for extracting data from scanned documents using artificial intelligence may be provided. Disclosed embodiments may involve obtaining at least one document associated with at least one entity and identifying at least one table located on the at least one document. Disclosed embodiments may involve extracting text within the identified at least one table based on predicted coordinates of the at least one table and reproducing the at least one table using the extracted text by: identifying at least one header of the at least one table; identifying a final row of the at least one table; identifying, using a sliding window technique, columns of the at least one table; and presenting the extracted text in rows and columns. Disclosed embodiments may store the reproduced at least one table in a database.