Synthetic fraud scenarios
Model fraud scenarios.Run them at decision speed.
FraudLab is a scenario engine: structured flows, explainable risk, and outputs fraud and risk teams can use—without live rails or customer data.
Education and defense only. All runs are synthetic models in software.
Pipeline
Four stages per scenario
- Context
Actors, channel, and starting signals
- Interaction
Multi-step flows with branches
- Decision
Rules, scoring, and flags
- Outcome
Impact, signals, and trace
Product
One engine for how fraud moves
Turn typologies into executable graph scenarios—reproducible runs for training, control design, and leadership briefings.
Graph scenarios
Encode flows as context, steps, rules, and outcomes—one engine, reproducible runs.
Explainable output
Risk, signals, narrative, and a decision trace so teams see what drove each result.
API-ready
Same runs in the lab or over HTTP: scenario id in, structured JSON out.
Synthetic only
Training and defense—not live abuse. No customer payloads or production rails.
Who it's for
For fraud and risk teams
Fraud ops, fincrime intel, trust & safety, and security training—rehearse high-stakes flows without live exposure.
- /Fraud & AML analysts
- /Risk & controls engineering
- /Incident response
- /Executive readiness briefings
Kncok
Kncok fraud risk
FraudLab is where we model synthetic typologies and defensive scenarios before they touch product or operations.
Safety
- No live payments or customer records.
- Models are for education and defense—not abuse playbooks.
- Outputs support awareness, controls, and response design.