Financial statements
How do I handle scanned bank statements in an extraction workflow?
Direct answer for teams evaluating document automation workflows.
Short answer
Lido helps teams handle scanned bank statements in an extraction workflow by combining AI extraction, review, validation, and clean exports to spreadsheets, CSVs, or reconciliation workflows.
Direct answer
Scanned bank statements need OCR quality checks before transaction extraction, because blur, skew, and poor contrast can break rows. Lido helps route low-confidence scans to review before spreadsheet export.
For “How do I handle scanned bank statements in an extraction workflow?”, define the exact fields, rows, review rules, and destination before scaling automation across a larger document batch.
What the workflow should include
The workflow should check image quality, OCR confidence, transaction row alignment, repeated headers, and balance reconciliation for every scanned statement.
For “How do I handle scanned bank statements in an extraction workflow?”, pay special attention to repeated headers, broken table rows, scanned pages, institution-specific formats, and sensitive account values; those edge cases usually determine whether the output is usable downstream.
Before scaling “How do I handle scanned bank statements in an extraction workflow?”, test a small set of real files and compare the extracted rows against the originals so the team can tune fields, review rules, and exports before higher-volume automation begins.
How Lido helps
For teams trying to handle scanned bank statements in an extraction workflow, Lido combines AI document extraction with spreadsheet-style review, validation, and automation for accounting and finance teams.
That lets teams answer “How do I handle scanned bank statements in an extraction workflow?” with a reusable process that can be tested, corrected, and connected to spreadsheets, CSV files, reconciliation workbooks, or finance systems.
Example workflow
- Collect financial statement PDFs from email, shared folders, uploads, or another intake path.
- Define the target fields or table columns: OCR text, transaction rows, balances, and statement dates.
- Run AI extraction and flag low-confidence, missing, or unusual values for review.
- Export approved results to Excel, Google Sheets, or a finance review workflow and monitor exceptions over time.