BANK STATEMENT → JSON

Bank statement PDF to structured JSON.

A typed schema you can trust. Every transaction is a well-formed object with ISO dates, numeric amounts, and normalised fields — ready to pipe into pandas, PowerBI, your API, or any database.

Convert your first PDF See pricing
Free to try · JSON export on Pro
Typed schema, ISO dates
Ready for pandas / SQL / APIs
HOW IT WORKS

PDF in, JSON out — three steps.

Upload the PDF
From your app, dashboard, or a manual test — same endpoint.
Extracts everything instantly
Statement meta + every transaction, normalised.
Download or POST
Get a JSON file, or (Firm plan) use our REST API to skip the browser entirely.
WHAT YOU GET

A clean JSON file, ready to use.

Every column laid out consistently. No cleanup, no manual re-formatting.

statement_bankxl.json
date (ISO)descriptiondebit (number)credit (number)balance (number)ref_no
01/04/2025NEFT-CR-INFOSYS LTD-SAL APR2025—85,000.001,35,000.00—
05/04/2025EMI-HDFC HOME LOAN-HOUSING LOAN32,500.00—1,02,500.00—
07/04/2025NEFT-DR-MAHESH LANDLORD-RENT35,000.00—67,500.00—
12/04/2025UPI-ZOMATO-zomato@paytm-MUM1,425.05—66,074.95—
15/04/2025ATM WDL-HDFC0001-ANDHERI5,000.00—61,074.95—
WHY BANKXL FOR JSON

Built for real accounting workflows.

Predictable schema
Every response has the same shape: { meta: {...}, transactions: [...] }. Meta includes bank_name, account_no, IFSC, period_from/to, opening/closing balance. Each transaction has date (YYYY-MM-DD), description, debit, credit, balance, ref_no.
Numbers are numbers
Amounts are JSON numbers, not strings. Dates are ISO 8601. Nulls where data is genuinely missing. No parsing gymnastics on your side — just JSON.parse() and go.
Works with pandas
One line: pd.json_normalize(response["transactions"]) and you've got a DataFrame ready for analysis.
REST API available
Firm plan customers get a REST endpoint. POST a PDF, GET JSON back. Perfect for pipelines.
Idempotent output
Same PDF in, same JSON out. Great for testing and pipelines.
Bulk-friendly
Small file sizes. Batch-process dozens of statements without paginating downloads.
WHO'S THIS FOR

Purpose-built for people who spend real time on bank data.

Fintech developers
Skip building your own OCR + parsing pipeline. Get clean, typed transaction data as an API response.
Data analysts
Straight into pandas / PowerBI / Tableau. Categorise, aggregate, chart.
CA-firm devs
Automate month-end for your firm — pipe JSON into your practice-management software.
Lenders & underwriters
Feed transaction data into cash-flow scoring, DSCR calculators, or ML models.
FAQ

Frequently asked.

What does the JSON structure look like?

{ "meta": { "bank_name": "HDFC Bank", "account_no": "50100...", "ifsc": "HDFC0001234", "period_from": "2025-04-01", "period_to": "2025-04-30", "opening_balance": 50000, "closing_balance": 61074.95, "currency": "INR" }, "transactions": [ { "date": "2025-04-01", "description": "NEFT-CR-INFOSYS LTD-SAL APR2025", "debit": null, "credit": 85000, "balance": 135000, "ref_no": "N91185..." }, ... ] }

Do you have a REST API for this?

Yes. Firm-plan customers get a REST endpoint that accepts a PDF via multipart/form-data and returns JSON. Documented at /api-docs.

What about date and number formats?

Dates are ISO 8601 strings (YYYY-MM-DD). Amounts are JSON numbers, not strings. Missing data is null (never empty string, never zero as a placeholder). You can JSON.parse() and use it without preprocessing.

Can I use this with pandas / Excel / PowerBI?

Yes. Pandas: pd.json_normalize(data["transactions"]). Excel/PowerBI: import the JSON via Power Query. Any tool that reads JSON works.

Is JSON export free?

It requires the Pro plan (₹499/month, 800 pages). Free users get Excel. For high-volume API access, see the Firm plan.

Try it with your own PDF.

30 free pages every month, no credit card. Upgrade only when you actually need more.

OTHER FORMATS
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