Zero setup, zero training
No templates, no labeled data, no schema files. Drop the document in and Docusift returns clean structured data.
Bank Statements extraction
Extract Tables from Bank Statements
Pull every tables value out of any bank statements — including scans and multi-page variants — into clean structured JSON. Each value carries a confidence score and a citation back to its source pixel.
Some statements carry more than one table — the transaction ledger, a fees summary, an interest schedule. Docusift detects each one and preserves its columns instead of merging them into a single scrambled block.
Docusift reads the statement the way a person does — it ties each amount to its own date and description, keeps the running balance aligned down every page, and splits debits from credits into their own columns. Multi-page statements and wrapped descriptions come out as clean, reconciled rows.
Each extracted value ships with a per-field confidence score and a citation back to the exact spot on the page it was read from, so low-confidence tables route to review automatically — and the clean data pushes to Google Sheets, a webhook, QuickBooks, or Xero.
| Column | Detected type | Sample value |
|---|---|---|
| Date | date | 2026-04-05 |
| Description | text | CARD 4471 — AWS EMEA |
| Amount | currency | -212.40 |
| Balance | currency | 7,570.15 |
Why teams choose Docusift
A confidence score on every field
Every value ships with a confidence score and a citation back to the source pixel in the original document, so low-confidence fields route to review instead of landing unchecked.
Fast turnaround
Seconds per document, not minutes. Built for production pipelines, not batch jobs.
Privacy first
Workspace-isolated processing, encrypted in transit and at rest, and one-click data deletion.
Frequently asked questions
How do I extract tables from bank statements?
Upload the bank statements to Docusift via the dashboard, the REST API, or by emailing it to your workspace inbox. Docusift returns structured JSON with every tables value, its confidence score, and a citation back to the source pixel — typically in under a second per page.
What is the best way to pull tables from bank statements automatically?
The best way is a tool that reads layouts visually rather than matching templates. Template tools break when a vendor changes their format; Docusift parses each bank statement from scratch, so it works on multi-page, multi-currency, and scanned variants without per-vendor configuration.
Does it work on scanned bank statements?
Yes. OCR, layout analysis, and field extraction run in a single pass, so scans, mobile photos, and native PDFs use the same endpoint and reach the same accuracy bar.
Can I push the extracted tables into my own system?
Yes. Receive structured JSON via REST, hit a webhook, or sync directly into Google Sheets, your data warehouse, or your accounting tool.
How accurate is Docusift at extracting tables?
Docusift reads each bank statement visually rather than matching a template, and every extracted value ships with a per-field confidence score and a citation back to the source pixel — so low-confidence tables route to human review automatically instead of landing unchecked.
Do I need to train a model first?
No. Docusift recognizes hundreds of document types out of the box. Custom fields are configured with a single sentence — no labeled training data required.
How is pricing calculated?
Pay per page processed. There is a free tier for evaluation and volume discounts for production workloads. No seat fees.
Can Docusift handle scanned or photographed documents?
Yes. The pipeline runs OCR + layout analysis + extraction in one pass, so scans, mobile photos, and native PDFs all flow through the same endpoint.
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