Synthetic Data for Insurance
Insurance testing needs a known claim rate and payouts that stay within coverage. Misata lets you declare the claim frequency so it comes out exactly, keeps claim dates after policy start, and ties every claim to a real policy and policyholder.
The tables Misata generates
policyholdersDemographics, risk tiers, policy-count roll-upspoliciesCoverage types, premiums, start and end datesclaimsFiled at a declared rate, payout within coverage, date after policy startpayoutsSettlements consistent with the claimWhat holds true, every time
- Claim rate matches your declared target proportion
- Claim dates fall within the policy's active window
- Payouts respect the policy's coverage limit
- Every claim references a real policy and policyholder
Frequently asked
Do I need real insurance data to generate this?
No. Misata builds the dataset from a specification, not a sample. There is no real insurance data to source, anonymize, or leak. You describe the tables you need and the engine constructs them with referential integrity and realistic distributions.
Is the generated insurance data privacy safe?
Yes, by construction. Nothing is learned from real records, so there is no membership to infer and nothing to leak. It runs entirely on your machine with no API key for the core engine.
Can I control the outcomes, like rates and totals?
Yes. Declare a target such as a monthly volume curve or an event rate and Misata produces rows that hit it exactly, while foreign keys stay intact and roll-up columns reconcile after a JOIN.
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