Convert Bank Statement PDF to JSON
Turn unstructured bank statement PDFs into clean, typed JSON — transaction arrays ready for your code, not your spreadsheet.
Built for developers and data engineers. Skip the PDF parsing headache. Free online tool.
How It Works
Upload a Statement
Any bank, any format — scanned, digital, password-protected. Drop the PDF and go.
AI Parses the Table
Transactions extracted into structured objects with typed fields — no regex, no pdfplumber, no Tesseract setup
Download JSON
Array of transaction objects with headers, count, and timestamp. JSON.parse() and go.
What the Output Looks Like
{
"headers": ["Date", "Description", "Debit", "Credit", "Balance"],
"transactions": [
["01/15/2026", "AMAZON MARKETPLACE", "49.99", "", "2,450.01"],
["01/16/2026", "DIRECT DEPOSIT - PAYROLL", "", "3,200.00", "5,650.01"],
["01/17/2026", "ELECTRIC COMPANY AUTOPAY", "142.30", "", "5,507.71"]
],
"totalTransactions": 3,
"exportedAt": "2026-01-20T14:30:00.000Z"
}Actual output mirrors your statement's columns. Headers vary by bank.
What is PDF?
Portable Document Format
Banks publish statements as PDFs — fixed-layout documents with no machine-readable structure. Parsing them requires understanding the visual layout, not just reading text. That's why regex and pdftotext fall apart on real bank statements.
What is JSON?
JavaScript Object Notation
Lightweight, language-agnostic data format. Every major language has a native parser. Ideal for API payloads, database inserts, dashboard data sources, and anywhere you need structured transaction data in code.
Why This Tool
Skip Building a PDF Parser
No pdfplumber, no Tabula, no Tesseract, no per-bank regex. Upload the PDF — get structured JSON back.
Typed Transaction Fields
Dates, amounts, and descriptions come as properly structured data — not raw text strings you need to clean and split yourself
Works With Any Bank Layout
No templates or column-mapping config per bank. The AI figures out the table structure from Chase, HSBC, HDFC, or any bank automatically.
Scanned PDF Support
Built-in OCR replaces your Tesseract + OpenCV pipeline. Handles scanned statements, faxes, and photos of printed pages.
Single Array, All Pages
A 40-page statement becomes one flat transaction array. No pagination, no per-page objects — just iterate and process.
Standard RFC 8259 JSON
Valid JSON that works with JSON.parse(), json.loads(), jq, and every database client. No proprietary format.
When to Use This
Fintech Data Pipelines
Ingest bank transactions into your platform — lending decisions, expense tracking, cash flow analysis, or fraud detection
Automated Reconciliation
Compare bank transactions against your ledger programmatically — match by date, amount, and description in a script
Custom Dashboards
Feed transaction JSON into D3.js, Chart.js, or Grafana to build financial dashboards without manual data prep
What Developers Build With This
Ingest
- PostgreSQL / MySQL inserts
- MongoDB document storage
- Webhook payload delivery
- S3 or GCS archival
Process
- Python / pandas DataFrames
- Node.js transform streams
- Reconciliation scripts
- Categorization with ML
Visualize
- D3.js / Chart.js dashboards
- Grafana panels
- React/Vue data tables
- Jupyter notebook analysis
Why Not Build Your Own PDF Parser?
pdfplumber / Tabula work for one bank — break on another
Bank statements have no standard layout. A parser tuned for Chase will fail on HSBC. You end up maintaining per-bank templates and regex patterns. Our AI generalizes across all banks.
Tesseract OCR output needs heavy post-processing
Raw OCR text has no table structure — just lines of text with inconsistent spacing. You still need to figure out which text belongs to which column. We handle OCR + table reconstruction in one step.
Multi-line descriptions split across rows
When a payee name wraps to two lines, naive parsers create two transactions. Our AI understands that "AMAZON MARKETPLACE" and "SEATTLE WA" on the next line are one transaction, not two.
Date formats vary by bank and country
MM/DD/YYYY, DD/MM/YYYY, YYYY-MM-DD, "Jan 15, 2026" — banks use them all. Writing a universal date parser is its own project. We handle all formats and output them consistently.
Frequently Asked Questions
What does the JSON output look like?
An object with `headers` (array of column names), `transactions` (array of row arrays), `totalTransactions` (count), and `exportedAt` (ISO timestamp). Each transaction maps to the headers by index.
Why not just build my own parser with pdfplumber or Tabula?
You can — for one bank. But bank statements have no standard layout. Every bank uses different column names, date formats, and table structures. Our AI handles all of them without per-bank templates or regex patterns.
Can I feed this into a database directly?
Yes. Map the JSON fields to your table columns and INSERT. Works with PostgreSQL, MongoDB, MySQL, DynamoDB — anything that accepts JSON or structured inserts.
What languages can parse the output?
Any language with a JSON parser — JavaScript (JSON.parse), Python (json.loads), Go, Ruby, PHP, Java, C#, Rust. It's standard RFC 8259 JSON.
Does it handle scanned or image-based PDFs?
Yes. Built-in OCR processes scanned statements and photos. No separate Tesseract or Google Vision setup needed.
Is there an API I can call programmatically?
Not yet — this is a browser-based tool. If you need an API endpoint for batch processing, reach out via our support page.
What about multi-page statements?
All pages are parsed into a single transaction array. No pagination, no splitting — one JSON object with every transaction.
Is my data secure?
Encrypted in transit, processed in memory, never stored. Your bank data is deleted after conversion completes.
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