Insight
Sub-second
Scoring latency. Decisions fit inside checkout and authorization flows.
Use case
Fraud detection scoring engine
Radiantze trains real-time fraud scores that adapt as patterns shift, with tiered approve, verify, or block decisions.
Catch new fraud patterns without blocking good customers
Radiantze trains a real-time scoring model on labeled transactions, learning timing, device, location, and account signals that fixed rule sets alone cannot cover.
Each authorization routes through tiered approve, verify, or block logic under a sub-second latency budget, and confirmed fraud or false positives feed the next retrain cycle.
Insight
Tiered
Response logic. Only highest-risk transactions blocked outright.
Insight
Feedback loop
Adaptation. Confirmed fraud and false positives feed retraining.
Partnership
Radiantze rollout pattern
Fraud models need tight integration with the transaction flow and thresholds aligned to loss versus friction tolerance.
- Assemble labeled historical fraud and legitimate transactions.
- Define tiered response thresholds with fraud and product teams.
- Integrate scoring into the authorization path under latency budget.
- Pilot with manual review on borderline scores.
Outcome
Enterprise outcome
Fewer fraud losses with lower false-positive rates than a static rule set alone, while checkout stays fast.
Radiantze capability stack
Real-time scorer
Feature assembly and model inference at transaction time within the authorization latency budget.
Tiered decisions
Approve, step-up verification, or block by risk band so good customers keep a smooth path.
Analyst review queue
Borderline cases held for human decision with full transaction context and score explanation.