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Fraud & Risk
Catch the fraud model that looks brilliant in a deck and fails on your data.
Train, bake off and stress-test fraud and risk models against deliberately messy, labelled synthetic data, so the decision is made on evidence, not optimism.
10x
faster than a production pilot
0
real customer records exposed
Messy
data on purpose
The problem
A model that scores well on clean data tells you almost nothing.
Real fraud arrives as missing fields, conflicting sources and adversarial edge cases. A vendor demo runs the happy path; production does not. The only meaningful test puts candidate models against representative, deliberately messy data and measures how they degrade. That is impossible against live customer records and unthinkable in production, which is exactly the gap NayaOne fills.
What teams prove here
Every test runs in an air-gapped workspace, on representative data, with an evidence pack at the end.
Fraud detection models
Benchmark vendor and in-house models on identical labelled fraud data, comparing detection, false positives and drift.
Payments & scam risk
Test real-time scoring and APP-scam controls against synthetic payment flows before they touch a customer.
Edge-case resilience
Deliberately introduce missing records and anomalies to see how a model holds up against reality, not a rehearsal.
In-house vs vendor
Prove a Claude or in-house build against a bought solution on the same data, and let evidence settle build-versus-buy.
The data
Representative by default. Real only if you choose.
Synthetic datasets come pre-loaded, so evaluation starts on day one with zero exposure.
Labelled synthetic fraud transactions
Synthetic payment & card flows
Deliberately messy multi-source records
Straight answers
Q.
How representative is synthetic fraud data?
It replicates the distributions, correlations and edge cases of real fraud, including the messy, incomplete conditions that break naive models, without exposing a single real record.
Q.
Can we compare our own model against vendors?
Yes. That is one of the most common uses, an identical-data bake-off between an in-house build and external tools, decided on measured performance.
Q.
What do we leave with?
An evidence pack risk and model-governance teams accept, and a clear path to move the winner from POC toward production.
Prove it on your use case.
A 30-minute walkthrough, scoped to exactly what you’re trying to evaluate.