This is like giving your anti–money laundering (AML) team a tireless digital analyst that reads every transaction, flags suspicious behavior, and prepares case files so humans only focus on the truly risky activity.
Manual AML monitoring and investigations are slow, expensive, and error‑prone. AI-driven AML automation reduces false positives, accelerates alert handling, and helps financial institutions keep up with growing transaction volumes and regulatory expectations.
Tight integration with internal transaction data and customer profiles, plus institution-specific tuning of detection rules and models, create a proprietary risk-detection layer that becomes hard to replicate.
Hybrid
Vector Search
High (Custom Models/Infra)
Model training and inference over very large, streaming transaction datasets while maintaining low latency and strong data privacy controls.