Accuracy

Accuracy depends on the statement type, not just the AI

The cleanest inputs are digital PDF statements with selectable text. The hardest inputs are low-quality scans and phone photos. Accuracy should be evaluated in stages: OCR, parsing, categorization, and final export.

Reviewed March 29, 2026

This page explains accuracy as an operational concept, not a single vanity percentage. It is meant to help buyers and operators understand where review is still useful.

How It Works
Pipeline overview from upload to structured output.
Bank Statement OCR
Scan-heavy input scenarios and OCR-specific workflow.
Extract Data from Bank Statement
What fields are extracted and where exports come from.
Stage
Best Case
What To Review
Text extraction
Digital PDFs with selectable text
Broken rows, merged merchant descriptions, missing balance fields
OCR recovery
Clean scanned statements with high contrast
Blurred digits, clipped table edges, repeated header rows
Transaction parsing
Consistent date and amount columns
Multi-line descriptions, debit/credit sign direction, statement summaries mixed with rows
AI categorization
Recognizable merchants and stable transaction patterns
Ambiguous transfers, niche merchants, mixed personal/business purchases

Practical Takeaway

Use digital PDFs whenever possible

If your bank offers both a downloadable PDF and a printed statement photo, always use the native PDF. It reduces OCR dependency, preserves the table structure, and usually gives the strongest extraction results.

Best Next Step

Match the workflow to the outcome you need

Buyers looking for the cleanest export should usually start with the converter and extraction pages. Buyers who want categorization and recommendations should start with the analyzer.

ConverterExtractionAnalyzer