Signals & decision services
Define the combination of rules, velocity checks and machine-learning models appropriate to each channel. Agree feature freshness and fallback behaviour when a decision service is unavailable.
Intelligence with oversight
Detect risk in the context of each transaction.
Bring payment behaviour, device signals and operational context into a coordinated risk assessment. Help risk teams identify suspicious patterns and prioritize investigation, with automation governed by accountable human oversight.
Technical engagement brief
Architecture decisions
that shape the solution.
We define the implementation around your systems, operating model and acceptance criteria.
Policy-controlled intervention
Map risk scores to allow, step-up, review or decline outcomes within the institution’s authority. Preserve decision context, analyst overrides and escalation records.
Model evaluation & monitoring
Evaluate confirmed fraud loss, recall, precision and false-positive rate together. Define comparable cohorts, matured outcome labels and model drift monitoring.
Compliance & responsibility
Data minimization, access controls and documented review govern consequential automated decisions. Fraud detection and AML monitoring have distinct responsibilities.
Scalability & performance
Separate online decision services from model training and analytical workloads. Validate the combined transaction-path latency budget under representative load.
From requirements to architecture
Let’s define the right operating model.
Stronger investigation priorities, with visibility into both fraud exposure and legitimate-customer friction.