Combines configurable rules and explainable AI to monitor transactions for money-laundering risk, with contextual alerts and integrated investigative workflows.

Collects and analyzes information across existing systems to prepare AML investigations, giving analysts contextual evidence while retaining human judgment for high-risk decisions.

Compiles evidence for customer-screening hits, assesses materiality against institutional policy, and triages alerts with cited reasoning and configurable human review.

Scores customer risk using static attributes, behavioral changes, and confirmed financial-crime signals, with configurable review schedules and regional risk models.

Adds behavioral risk scoring and explainable AI to existing AML systems, returning prioritized alerts without replacing the underlying monitoring infrastructure.

Checks individuals and organizations against sanctions, politically exposed person, and adverse-media lists using configurable fuzzy matching and AI-assisted false-positive reduction.