Use case
Lending decisions on cited transactions, not OCR dumps
Multi-page statements become stitched transaction tables — every row traceable to page and bbox.
The problem
Credit files live or die on transaction history, but statements arrive in every bank's layout and span dozens of pages.
- 01Layout sprawl. Each bank formats statements differently. Per-bank templates rot with every redesign.
- 02Page breaks mid-table. Transaction tables run across pages; naive extraction drops rows or duplicates headers.
- 03Manual spreading. Analysts retype transactions into scorecards. Slow, expensive and error-prone.
- 04Unverifiable rows. A transaction in a spreadsheet with no source link is an audit finding waiting to happen.
The solution
Fieldry turns any statement into a stitched, cited transaction dataset ready for scorecards.
- 01Bank-agnostic rows. Date, description, amount and balance extracted from any layout to one schema.
- 02Stitched tables. Multi-page tables joined with balance continuity; header rows never leak into data.
- 03Cited everything. Each transaction links to page and bbox — underwriters verify without opening PDFs.
- 04Cash-flow ready. Clean rows feed income estimation, categorization and affordability models directly.
Extraction checks
Search the statement checks.
See what each check returns.
Every check Fieldry runs on a bank statement.
Bank-agnostic rows
Date, description, amount and balance extracted from any layout to one schema.
Stitched tables
Multi-page tables joined with balance continuity; header rows never leak into data.
Cited everything
Each transaction links to page and bbox — underwriters verify without opening PDFs.
Command preview
Stitched rows
Statement lines become structured rows with running-balance continuity across pages.
Anatomy of a statement file
An applicant uploads a 40-page statement: scanned pages, a mid-table page break every few sheets, and one bank's idiosyncratic date format.
Fieldry stitches the transaction table, reconciles balances, screens for edits — and hands underwriting cited rows.
- Input40-page multi-bank PDFs
- OutputOne cited transaction table
- ReviewExceptions only, not every row
From PDF pile to scorecard rows
Underwriter works from cited rows; exceptions carry their evidence.
AnalystForensics and consistency checks run; gaps and edits flagged.
FieldryTransaction tables extracted and stitched with balance continuity.
FieldryStatements arrive in mixed layouts, scanned quality varying by page.
Document
4 milestones, newest first.
Key benefits
Credit files built on transactions you can point at.
Same-day spreading
Upload to underwriting-ready rows in one call instead of a week of retyping.
Any bank
New layout parses against the same schema on first sight — no template queue.
Audit-proof rows
Each transaction links to page and bbox; sampling takes minutes.
Bulk portfolios
Backlog statements drain through batch at half price; live applications stay sync.
Spread your toughest statement
Run a real multi-page statement on free credits and check the rows against the PDF.
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Rows join across page breaks with repeated-header detection and running-balance continuity, so headers never leak into data and gaps surface as missing pages.
File, image and consistency checks flag alterations and font anomalies before the file reaches credit — with evidence, not verdicts.
Clean rows feed your categorization and cash-flow models directly; Fieldry delivers the cited dataset, your models do the scoring.
Click the transaction to see its page, source text and bounding box — no PDF scrolling.
Parse your first document today
One API for extraction, evidence and automation.