Bank Statement OCR: Extracting Transaction Data Across 50+ Global Banks

Every bank formats statements differently. Chase uses one table style, HSBC another, MUFG a third — with different date formats, currency symbols, transaction descriptions, and column orders. Hard-coding a parser per bank does not scale; that is why bank-statement OCR has historically been expensive.

Layout understanding beats templates

Instead of template matching, CrazyOCR's parser identifies the table structure of a statement and extracts rows semantically: date, description, debit, credit, balance. Because it understands layout rather than a fixed format, the same engine handles Chase, HSBC, Wells Fargo, Santander, MUFG, and dozens more banks without per-bank templates.

From statement to spreadsheet

Extract a statement and export it as CSV or Excel in one click — each transaction on its own row, columns aligned. This is the workflow behind bookkeepers, expense teams, and loan processors who used to retype statements by hand.

Upload a statement on the homepage to see column-perfect extraction yourself — no template configuration required.