Finance and accounting decision
Bankstatemently
A competent engineer can build a basic PDF→CSV statement parser and API (multi-week, ~160h), but reproducing Bankstatemently's verified per-bank templates, published accuracy benchmark, multi-language/forensic polish, and enterprise features would require more work or using their open benchmark and additional data/models.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$20one-off6 h to build
$0/mo6 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Bankstatemently alternatives, with the arithmetic →
What a replacement has to do
- Upload PDF → extract text/tables → detect accounts & transactions → normalize amounts/dates → export CSV/Excel/JSON
What it still won’t have
- Verified templates and per-bank optimizations for 361+ banks
- Published accuracy benchmark and evaluation framework
- Multi-language, scanned-document and forensic features tuned for legal/compliance workflows
- Built-in integrations and exports tailored to QuickBooks/Xero with tested templates
- Enterprise support, SLA, and priority processing
What remains hard
- Execution quality
The only bank statement converter with a published accuracy benchmark
First-year cost
No published price
Bankstatemently does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal bank-statement-to-CSV service using Python (FastAPI), Postgres, React, and Tesseract OCR. Core features in scope: secure PDF upload (S3), OCR preprocessing (deskew, denoise), table/region detection on each PDF page, transaction extraction and normalization (date, description, amount, balance), simple reconciliation checks, REST API returning JSON and endpoints to download CSV/XLSX/QBO exports, and a basic React web UI to upload and review results. Out of scope: training custom ML models for per-bank templates, multi-language model tuning beyond Tesseract defaults, enterprise billing, and SLA support. Include logging, error handling, unit tests for parsing logic, and a Docker Compose dev + production deployment manifest (NGINX, app, DB).
How we checked
How the score was reached
- Self-host verdict base92
- An open-source build was found+5
- 5 cited sources+3
- Evidence score99
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productBankstatemently — Bank Statement Converter | PDF to CSV
- official pricingBankstatemently Pricing — A Bank Statement Converter
- official docsBank Statement Analysis Use Cases by Industry
- open sourcemayswind/ezbookkeeping
- open sourceandrewdwallo/erpsaas
Integrity checks
What held up, and what did not.
