Core transaction fields
Organize transaction dates, descriptions, money in, money out, and balances into consistent columns.
Bank statement data extraction
Extract bank statement transactions into structured data
Turn PDF bank statement tables into structured transaction rows for reconciliation, bookkeeping preparation, or downstream spreadsheet work.
5 pages free · No payment card required
Field mapping preview
Statement text → transaction model
| Statement fragment | Structured field | Reviewed value |
|---|---|---|
| 03/03/26 | Transaction date | 2026-03-03 |
| GROCERY STORE | Description | Grocery store |
| 125.50 DR | Money out | 125.50 |
| 2,374.50 | Balance | 2,374.50 |
Synthetic fragments demonstrate the data model; actual fields depend on what is present in the source statement.
1. Read table
2. Map fields
3. Review values
Review-ready output
Structured data you can check before using.
Extracting transactions from bank statements is more than copying text. A useful result needs dates, descriptions, debit and credit amounts, and balances separated into dependable fields while statement summaries and page noise stay out of the table.
RasterLift creates a review workspace for each PDF. You can inspect the structured rows, correct recognition issues, resolve flagged values, and only then export the transaction data to CSV or Excel.
Review against the source
Compare extracted rows with a private, read-only view of the original statement.
Correct before download
Edit recognition errors and resolve review flags before creating the final file.
Individual or batch export
Download one statement or selected completed statements as CSV, Excel, or packaged files.
Inspect the field mapping
Verify how statement text becomes transaction data.
A flat text dump is not enough. RasterLift separates each transaction into named fields and keeps uncertain values available for human review before export.
Named transaction fields
Dates, descriptions, money out, money in, and balances have distinct places in the transaction model.
Money-direction separation
Debit and credit values remain distinguishable instead of being inferred later from an unstructured amount string.
Balance context
Available running balances stay attached to their rows so the sequence can be compared with the statement.
Review provenance
The source page and review state provide context for fields that need confirmation or correction.
Practical workflows
Turn statement tables into fields a reviewer can verify.
Extraction is most useful when every value has a clear place in the transaction model and uncertain rows remain visible instead of disappearing into a flat text dump.
Field normalization
Separate transaction dates, descriptions, money in, money out, and balances into consistent columns across the statement.
Balance tracing
Keep available balance values beside their transaction rows so reviewers can compare the extracted sequence with the source pages.
Exception review
Surface incomplete or low-confidence values for correction before the transaction set is exported to CSV or Excel.
FAQ
Questions about this conversion.
Which transactions can RasterLift extract?
RasterLift extracts transaction rows from PDF bank and credit card statements, including dates, descriptions, money in, money out, and balances where available.
Can I verify transactions before export?
Yes. Each document has a review workspace where extracted fields can be checked and corrected.
Can multiple statements be processed?
Yes. You can upload multiple PDF statements, review them separately, and export selected completed results.
Does RasterLift edit the original statement?
No. Original PDFs remain read-only and are scheduled for automatic deletion after 24 hours.
Related workflows