Automated Bank Statement Extraction for Real-Time Risk Analysis
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Solution Overview
Problem
Traditional methods for extracting information from bank statements, such as manual analysis and cloud-based solutions, are time-consuming, error-prone, and raise data privacy concerns, while vendor-based solutions often fail to provide real-time results, hindering financial risk assessment and customer experience.
Innovation Solution
An automated document extraction system that identifies and extracts transaction and ownership data from bank statements in a structured format, using a two-stage technique involving geometric structure analysis and named entity recognition to facilitate real-time financial risk analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis is used to extract information from bank statements, then data privacy is maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer vision system using optical character recognition (OCR) and machine learning algorithms. The system processes bank statements through automated image-to-text conversion, geometric structure analysis, and named entity recognition, eliminating human intervention while maintaining high extraction accuracy and enabling parallel processing of multiple documents simultaneously.
2Productivity
If cloud-based solutions are used for extraction, then processing speed increases, but data privacy concerns arise
Solution Approach 1:
The patent introduces an on-premises automated extraction system as an intermediary between the bank statements and the analysis processes. This local deployment architecture allows high-speed automated processing through OCR and machine learning while keeping all financial data within the organization's secure infrastructure, eliminating the need to transmit sensitive information to external cloud services.
3Extent of automation
If vendor-based solutions are used, then some automation is achieved, but real-time results are not provided
Solution Approach 1:
The patent segments the document processing into distinct automated stages: optical character recognition for text extraction, geometric structure analysis for layout understanding, and named entity recognition for data classification. This modular automated pipeline enables real-time processing by eliminating sequential manual steps, with each stage operating independently and efficiently to deliver immediate results.
4Adaptability or versatility
If manual resources are used for analyzing multiple bank statements, then customization for specific business needs is possible, but operational costs increase
Solution Approach 1:
The patent implements a dynamic automated extraction system where machine learning models are trained on organization-specific bank statement formats and can adapt to different business requirements. The system learns from labeled training data and can be retrained to accommodate new statement types, maintaining high customization capability while eliminating the ongoing operational costs of manual analysis teams.
Data Source
AI summary
Disclosed are various embodiments for extracting transaction and user data from financial documents and formatting the data into a structured format to facilitate a real-time analysis of the extracted data. A user may submit an unstructured formatted financial document with a credit rating request, underwriting request, and/or other type of financial risk assessment request. Text components and a table component are identified according to a structural representation of the document. The text components are analyzed to identify and extract ownership data associated with the user that can be used to verify ownership of the provided document by the submitting user. The transaction data is identified and extracted in a structured format based at least in part on a table header location and detected column boundaries. The extracted transaction data is validated to ensure an accurate extraction of the transaction data.