Profile
Map accounts, entities, relationships, sequences, constraints, sensitivity, and rare-event behaviour.
Financial services
Recreate financial behaviour without redistributing customer records.
Forge transaction, claims, credit, fraud, and risk cohorts with measurable fidelity and a documented privacy release gate.
02.1
Example constraint
A fraud model needs realistic entity networks, time sequences, and rare attack patterns, but production transaction data cannot enter the development environment.
Ready for qualification
A concrete foundry workflow
A fraud model needs realistic entity networks, time sequences, and rare attack patterns, but production transaction data cannot enter the development environment.
Forge
Generate new transaction networks and conditionally increase the scenarios the model must learn.
Validate
Compare distributions, graph structure, task performance, memorisation, and inference risk.
Release
Attach intended use, limitations, evidence, and the accountable reviewer to the accepted cohort.
Acceptance measureCoverage of risk-relevant behaviours
Baseline → threshold → observed result
Controls to scope
✓Restricted-field and source boundary
✓Rare-record and linkage risk
✓Downstream risk-model utility
✓Named data and model owners
Start with one release
Qualify the first financial services release.
Start with one restricted dataset, one model decision, and explicit acceptance criteria.
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