Is this real banking data?

No. Every amount, merchant, and account ID is randomly generated locally in your browser from sample pools. Nothing connects to a real bank and no data leaves your device.

What columns are included?

Each transaction has a transaction ID, account ID, ISO date, a signed amount, a currency code, a category, a merchant name, and a debit or credit type. Debits are negative and credits are positive.

How are amounts decided?

Amounts are random within ranges that suit each category, so groceries are small and rent or salary are large. Debits get a negative sign and credits a positive one, which keeps a running balance realistic if you sum them.

Can I get a balanced ledger?

The data is not double-entry balanced by default; it is a flat transaction list. You can sum the signed amounts to get a net cash flow, but it will not be zero unless you post offsetting entries yourself.

Is the output reproducible?

No. Each click regenerates a fresh random set. Save the output if you need a stable dataset for a demo or test fixture.

What is the Sample Finance Dataset Generator?

Generates synthetic financial transactions with amounts, dates, categories, merchants, and account IDs as CSV or JSON. Great for fintech dashboard prototypes, ledger tooling, and data-visualization demos without real banking data. It runs free in your browser on Gera Tools, with nothing uploaded.

Sample Finance Dataset Generator

Name: Sample Finance Dataset Generator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

This tool produces realistic but completely fake financial transactions so you can build fintech dashboards, ledger viewers, and charting demos without using real banking data. Everything is generated locally in your browser.

How it works

Each row is built by picking a category, then drawing an amount from a range that fits that category:

A transaction ID like TXN-000123 and an account ID like ACC-0007.
A category (Groceries, Dining, Rent, Salary, Transfer, Utilities, …) with a realistic amount range, so a salary is large and a coffee is small.
A debit/credit flag — debits are stored as negative amounts and credits as positive, so summing the column gives net cash flow.
A merchant name appropriate to the category and an ISO date within the past year.

The result is serialized as CSV with a header row or as pretty JSON.

What the generated data looks like

A sample of generated CSV rows (values are illustrative, generated fresh each time):

transaction_id,account_id,date,amount,currency,category,merchant,type
TXN-000001,ACC-0003,2024-11-14,-47.82,USD,Groceries,Whole Foods Market,debit
TXN-000002,ACC-0007,2024-11-15,2850.00,USD,Salary,Employer Direct Deposit,credit
TXN-000003,ACC-0003,2024-11-16,-12.50,USD,Dining,Blue Bottle Coffee,debit
TXN-000004,ACC-0011,2024-11-17,-1500.00,USD,Rent,Property Management Co,debit
TXN-000005,ACC-0007,2024-11-18,-38.40,USD,Utilities,Pacific Gas & Electric,debit

Realistic category ranges

The generator uses category-specific amount ranges so the data behaves sensibly in aggregate:

Category	Typical range	Type
Salary / payroll	$1,500 – $5,000	Credit
Rent	$800 – $2,500	Debit
Groceries	$15 – $180	Debit
Dining	$8 – $85	Debit
Utilities	$30 – $200	Debit
Transfer	$50 – $2,000	Credit or debit
Transport	$10 – $120	Debit

This range structure makes charts and aggregations look plausible — category totals are in realistic proportions relative to each other, which matters for budget-breakdown and spending-analysis demos.

What you can build with it

Dashboard prototypes: Load into a BI tool (Metabase, Superset, Tableau Public) to build a spending dashboard with working category breakdowns and trend lines.
Frontend charting demos: Feed the JSON directly into a React or Vue component to demo a transaction list with live filtering, sorting, and category pie charts.
Backend and API testing: Use as seed data for a ledger API — the account IDs and transaction IDs are consistent in format and allow joins.
Pagination testing: Generate 500–5,000 rows to test infinite scroll or table pagination under realistic data volumes.
Data pipeline demos: Use as sample input to show an ETL pipeline, data cleaning script, or categorization model.

Tips and notes

Sum the signed amount column to demo a running-balance or cash-flow chart. Debits are negative and credits are positive, so a cumulative sum gives a plausible net-cash-flow line. Because categories carry sensible ranges, the data works well for grouping, budgeting, and category-breakdown visualizations. Each generation is independently random, so save the file if you need a stable fixture for tests or a repeatable demo.