Use cases
Turn PDF bank statements into transaction-level data you can match, pivot, and export—native PDFs and scanned copies via OCR.
Account number, period, balances, and transaction rows—structured for reconciliation workflows.
A sample of the structured output for this document type.
Fields in your schema
Bank statement PDFs are built for humans, not spreadsheets. Reconciliation teams spend hours copying transaction rows by hand or fighting with inconsistent export formats.
Parsedit extracts the fields you define—account details, period, balances, and individual transactions—and structures them for matching and pivot tables.
Scanned statements run through OCR with the same schema as native digital PDFs. Your team reviews every value before export.
Approved transaction rows land in Google Sheets or your webhook, ready for reconciliation against invoices, ledgers, and internal records.
Purpose-built capabilities for this document type.
OCR handles scanned statements; native PDFs run through the same schema.
Date, description, and amount per row—for pivot tables and matching.
Push reconciled data into your reporting sheet or accounting system.
Ingest statements, extract transactions, and export for matching.
Upload statement PDFs from any major bank.
Parsedit reads statement tables and maps date, description, and amount columns to your fields—regardless of bank formatting.
Scanned PDFs and image uploads run through the same field schema as native digital statements.
OCR ExplainedSend approved transaction rows to Google Sheets and a webhook simultaneously—for reporting and your ledger.
Integrations
Export transaction rows to Google Sheets, Xero, QuickBooks, or custom webhooks.
Your documents stay in your account. Review is on by default, and auto-send only runs where you enable it.
No training on your data
Review on by default
Secure storage of extracted data
You stay in control
Common questions about this capability. Need more detail? our documentation
PDF statements from most banks, including scanned copies via OCR. The output structure follows the fields you define.
Yes. Extracted transactions export to Sheets or your webhook with date, description, and amount for matching and pivoting.
Use cases
Create your first parser in minutes. No code, no setup calls.
Capture period summaries alongside individual transactions.
Approve transactions before they sync—full audit trail included.
Map transactions, balances, and period metadata to your schema.
Export rows to Sheets or your system for matching and reporting.
Extract name, document number, and key dates from passports, national IDs, and licenses with review before send.