Misata: Outcome-Conformant Synthetic Data Generation

Muhammed Rasin

Synthetic Data for Credit Risk

Describe the lender and its book, for example 4,000 borrowers with personal loans and a 6% default rate, and Misata Studio builds borrowers, loans in current, delinquent, defaulted or paid-off states, and repayment schedules that amortise correctly. The default rate you state is exact and listed in the certificate. For risk-model work with regulatory parameters, the free credit-risk portfolio below comes with PD, LGD and EAD already worked out.

Updated 2026-09-24.

You would write

A consumer lender with 4,000 borrowers, personal and auto loans over three years, and a 6% default rate.

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

customersBorrowers whose names, cities and phones agree with the country

loansCurrent, delinquent, defaulted or paid off, at the rates you state

loan_paymentsAmortisation schedules; a paid-off loan ends at exactly zero

accounts and transactionsWhere the repayments come from, if you want them

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.

The default rate you state is exact
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

Free sample dataset

3,000 borrowers and 4,200 loans where PD comes from S&P's published default study, LGD from the Basel F-IRB table and EAD from the credit-conversion-factor formula. Studio does not compute these Basel parameters for you; the sample has them.

See the sample

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 credit risk 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 credit risk 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.

Does Studio compute PD, LGD and EAD?

Not as a built-in model. Studio makes the default rate you state exact and keeps repayment schedules correct. If you need Basel parameters, start from the free portfolio sample, which has them worked out from published numbers.

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