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Intelligent Parsing and Data Extraction Workflow for Bank Statements

This workflow can automatically download bank statement PDFs, split them into images, and use a visual language model to transcribe them into structured Markdown text, preserving table and text details. Next, it employs a large language model to extract key data from the statements, such as deposit records, addressing the accuracy issues of traditional OCR in complex layouts. This process significantly enhances the efficiency of parsing bank statements and is suitable for scenarios where financial personnel and fintech companies need to quickly process scanned documents.

Workflow Diagram
Intelligent Parsing and Data Extraction Workflow for Bank Statements Workflow diagram

Workflow Name

Intelligent Parsing and Data Extraction Workflow for Bank Statements

Key Features and Highlights

This workflow automatically downloads bank statement PDF files and splits them into images. It leverages advanced Vision Language Models (VLMs) to transcribe scanned or downloaded PDF pages into structured Markdown text, preserving table and text details to the greatest extent. Subsequently, Large Language Models (LLMs) are employed to accurately extract key data items from the statements, such as all deposit records, enabling intelligent understanding and data extraction from complex scanned documents.

Core Problems Addressed

Most bank statements are scanned PDFs, where traditional OCR struggles to accurately extract tables and complex layouts, resulting in low processing efficiency and high error rates. This workflow uses vision language models to recognize content within images and maintains the original document structure in Markdown format, significantly improving parsing accuracy for scanned PDFs and facilitating subsequent data extraction.

Application Scenarios

  • Automated processing of bank statements by finance personnel for rapid retrieval of key deposit information
  • Document management and data analysis systems requiring table data extraction from scanned or downloaded PDFs
  • Fintech companies and accounting service providers aiming to enhance document processing intelligence
  • Any business process that requires batch parsing of complex PDF documents with structured output

Main Process Steps

  1. Manually trigger the workflow execution.
  2. Download specified bank statement PDF files from Google Drive.
  3. Use the Stirling PDF service to split the PDF into multiple high-resolution JPEG images.
  4. Unzip the image archive and convert it into an image list.
  5. Sort images by filename and uniformly resize them to accelerate AI processing.
  6. Transcribe each page image into Markdown format text via Google Gemini Vision Language Model, preserving text, headings, and table structures.
  7. Aggregate Markdown texts from all pages.
  8. Use Google Gemini Language Model to extract all table rows containing deposit amounts and output structured deposit data.

Involved Systems or Services

  • Google Drive (file download)
  • Stirling PDF Webservice (PDF to images conversion)
  • n8n built-in nodes (file unzip, sorting, image editing, code processing, etc.)
  • Google Gemini (PaLM) Vision Language Model and Language Model APIs
  • Markdown format text processing

Target Users and Value

  • Financial analysts, accountants, auditors, and other professionals needing rapid processing of bank statement data
  • Fintech enterprises and developers of document automation solutions
  • Any organizations or individuals aiming to enhance scanned document recognition and structuring capabilities through AI
  • Users with high data privacy requirements (can deploy private PDF splitting service to replace third-party tools)

By using this workflow, users can achieve efficient and accurate automated parsing and key data extraction from scanned or downloaded bank statements, significantly reducing manual processing time, minimizing errors, and enhancing the intelligent management of financial data.