How do I categorize transactions after extracting bank statement data?

Why statement extraction is hard

Bank and credit card statements often include dense tables, page breaks, repeated headers, and institution-specific formatting.

The goal is not just to read the statement. The goal is to preserve rows, columns, dates, descriptions, balances, and totals accurately enough for reconciliation or analysis.

What to look for

A reliable setup should extract transaction descriptions, amounts, dates, and category fields, normalize the output, and send it to Excel, Google Sheets, or a finance review workflow without requiring someone to retype each row.

You also want a review step for ambiguous rows, scanned pages, or statements with unusual formatting.

Where Lido fits

Lido works well when financial documents need to become spreadsheet-ready data. Teams can define the output columns, review extracted rows, and automate the handoff.

That makes it a good fit for recurring statement processing, transaction analysis, and finance operations workflows.

Example workflow

1. Collect financial statement PDFs from email, shared folders, uploads, or another intake path.
2. Define the target fields or table columns: transaction descriptions, amounts, dates, and category fields.
3. Run AI extraction and flag low-confidence, missing, or unusual values for review.
4. Export approved results to Excel, Google Sheets, or a finance review workflow and monitor exceptions over time.