Less fraud. More Compliance.

Noysau develops compliance and risk products built on graph intelligence.

Risk often emerges through connections.

A transaction, customer or document tells only part of the story. Noysau connects data so fraud, compliance and risk teams gain the missing context.

Available with KER

Transaction fraud

Identify coordinated behaviour across accounts, devices and channels.

Available with KER

AML

Connect flows and focus reviews on sensitive cases.

Phase 2 or partnership

KYC

Resolve related identities, documents and entities.

Phase 2 or partnership

Credit scoring

Complement assessment with understandable relationship signals.

KER connects signals to action.

For payment service providers across WAEMU, KER brings fraud detection, AML analysis and case handling into one workspace.

DetectCombine business rules, scores and graph signals.
HoldApply authorised measures to high-confidence detections.
ReviewRoute uncertain cases to analysts, then risk managers.

Graph variables provide context that rules and conventional models cannot capture alone. They can help distinguish a community solidarity chain from a mule network.

Discover KER
Actual KER overview showing operational risk, alerts and cases requiring attention
Actual KER screenDemonstration environment using synthetic data

More useful signals for every team.

Noysau turns relationship complexity into readable, controlled and traceable decisions.

Context

See the relationships that give meaning to an isolated event.

Relevance

Focus human review on situations that require it.

Explainability

Retain the facts, factors and decision path.

Abdoulaye A. Gaye
« Our market needs compliance and risk management tools designed around its own financial ecosystem. The challenge is not merely to detect an unusual structure, but to understand its context: distinguish a legitimate neighbourhood savings circle from a network of mule accounts, then give your risk teams the evidence they need to make a sound decision. »

Abdoulaye A. GayeFounder | Machine Learning Engineer

Security is scoped before the pilot.

Residency, access, roles, return conditions and production requirements are reviewed with each institution.

See our security approach

Let’s discuss your priority risk.

We agree the need, authorised data and success criteria before any demonstration.

Book a demo