How do I categorize transactions after extracting bank statement data?

Direct answer

Categorize extracted bank transactions by applying merchant, description, amount, and account rules, then reviewing uncertain categories. Lido helps combine extraction with spreadsheet rules and exception review.

For “How do I categorize transactions after extracting bank statement data?”, 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 use category mappings, lookup tables, keyword rules, reviewer corrections, and reconciliation checks before exporting categorized transactions.

For “How do I categorize transactions after extracting bank statement data?”, 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 categorize transactions after extracting bank statement data?”, 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 categorize transactions after extracting bank statement data, Lido combines AI document extraction with spreadsheet-style review, validation, and automation for accounting and finance teams.

That lets teams answer “How do I categorize transactions after extracting bank statement data?” with a reusable process that can be tested, corrected, and connected to spreadsheets, CSV files, reconciliation workbooks, or finance systems.

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.