High-fidelity synthetic fraud data.
Designed for real fraud models. Delivered without requiring your sensitive transaction history.
Fraud is behaviour, not a random label.
Real fraud develops through identity, timing, intent, adaptation and escalation. IKARAS models those patterns across connected events so the resulting data can do more than simply look plausible.
Model training
Add labelled examples where real fraud is scarce, sensitive or difficult to share.
Model testing
Check how a model reacts to patterns it has not already memorised from historical data.
Stress testing
Increase fraud pressure, combine scenarios and explore edge cases before they arrive in production.
More control over the cases your model needs to learn from.
Historical data is valuable, but it only contains what has already happened — in the proportions it happened. Synthetic data gives fraud and risk teams a controlled way to add difficult, rare or deliberately challenging cases without waiting for those cases to appear naturally.
Choose the fraud behaviours, prevalence and combinations you want represented instead of being limited to the mix already present in historical data.
Datasets can be generated with known scenario labels and supporting metadata, making them easier to use for training, testing and analysis.
Increase examples of edge cases or low-frequency fraud patterns when real examples are too scarce to support robust experimentation.
Recreate a generation setup and compare model behaviour against controlled conditions rather than relying only on a changing historical sample.
You decide what the model should have to face.
Fraud rate, scenario weighting, behavioural overlap and drift can be shaped around the question you are trying to answer. That makes the dataset a testing instrument, not just extra volume.
More than a CSV.
A delivery can include the main transaction dataset, a fraud-only subset, scenario breakdown, validation summary, behavioural integrity checks, a reproducible configuration snapshot, run manifest and executive-ready report.
Account & identity compromise
Account takeover, synthetic identity, device/IP mismatch and geo-impossible activity.
Payments & transactions
Card testing, unusual velocity, coordinated spending and transaction laundering patterns.
Marketplace & platform abuse
Collusion, refund abuse, promo/referral abuse and seller behaviour shifts.
Drift & adaptation
Fraud patterns that change over time so models can be tested against a moving target.
From schema to usable dataset.
Schema
Fields, formats, constraints.
Risk goals
Scenarios, fraud rate, test objectives.
Generation
Behaviour, labels, drift and overlap.
Validation
Checks, documentation and delivery.
Questions teams usually ask.
Do you need raw customer transactions?
No. We can generate from the structure and objectives that define the environment, which reduces the need to share sensitive customer records.
Can you match our schema?
Yes. The generation can be mapped to the fields and constraints your model or workflow expects.
Can we choose the fraud mix?
Yes. Fraud prevalence, scenario weighting and overlap can be configured around your use case.
Is the data reproducible?
Projects can be delivered with a configuration snapshot and run information so a generation setup can be reproduced.
Is synthetic data a replacement for every form of real data?
No. It is a complementary tool for training, testing, scenario coverage and privacy-sensitive experimentation. The right mix depends on the use case.