Misata: Outcome-Conformant Synthetic Data Generation

Muhammed Rasin

Synthetic Data for Fintech

Describe the business, for example a neobank with 5,000 customers, a 6% loan default rate and a 0.3% fraud rate, and Misata Studio builds customers, checking and savings accounts, a transaction ledger, and loans with their repayment schedules. An account's balance is worked out from its transactions, the fraud rate is exact and lands on the transactions a fraudster would pick (online, larger), and every paid-off loan's schedule ends at exactly zero.

Updated 2026-09-24.

You would write

A neobank with 5,000 customers, checking and savings accounts, loans with a 6% default rate and a 0.3% fraud rate over three years.

Studio builds this on one of its tested archetypes: the mechanics below are fixed and verified, and what the business sells, the names and the wording are written for yours.

Start from this

What Studio builds

customersPeople whose names, cities and phones agree with the country

accountsActive, dormant or closed; the balance is the sum of the account's transactions

transactionsA timestamped ledger with a fraud label at the rate you state

loansCurrent, delinquent, defaulted or paid off, with the default rate you state

loan_paymentsAn amortisation schedule that ends at zero for every paid-off loan

What holds, and how you know

These are checked on the finished data, not assumed. Each dataset comes with a certificate listing what you asked for and whether it was met. How we verify.

Fraud and default rates you state are exact
Fraud lands on risky transactions, not at random, so a detector has something to learn
An account's balance equals the sum of its transactions
A paid-off loan's schedule ends at exactly zero
Every foreign key points at a row that exists, checked on the finished data
Every count, total, rate, share and date window you state is applied exactly, or listed as not applied
A certificate lists each requirement and whether the data meets it

Have a schema, specialised rules or a large volume and would rather hand it off? Ask us to build it for you.

Frequently asked

Do I need real fintech data to generate this?

No. Misata Studio builds the dataset from a description, a schema you draw, or a structure you import (SQL DDL, DBML or a CSV header). No real records are uploaded, copied or learned from, so there is nothing to anonymise.

Is the generated fintech data privacy safe?

Yes. Nothing is learned from real records, so no real person, customer or account can appear in the output. A model writes names, places and wording, and Studio may research public facts on the web (you can turn research off). It never asks for your data.

Can I control the numbers, like rates and totals?

Yes. State a total, a monthly curve, a share or a rate in plain words, or draw the curve, and Studio applies it exactly. The certificate lists each figure and whether it was met. If a figure cannot be met, Studio says so instead of changing it.

Can I use my own schema?

Yes. Paste SQL DDL or DBML, import a CSV structure, or draw the tables on the canvas. A schema you give is followed exactly, and any difference is reported.

Which formats can I export?

CSV, Excel, Parquet, JSON Lines, SQL for Postgres, MySQL, SQL Server, Oracle, BigQuery and Snowflake, SQLite, DuckDB, dbt seeds, Prisma, DBML, TypeScript, JSON Schema, a data dictionary and more.

Choosing a tool? How Misata compares

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