Think of this as a smart watchdog for banks: it constantly watches transactions and customer behavior, learns what “normal” looks like, and then flags suspicious activity that could be money laundering or fraud—much more accurately than old rules-based systems.
Traditional anti–money laundering (AML) and financial crime compliance tools generate huge numbers of false alerts, miss sophisticated schemes, and require large manual teams. This platform automates detection of suspicious activity, improves accuracy, and helps financial institutions meet regulatory requirements at lower cost and with less operational burden.
Domain-specific AML models trained on regulatory patterns and financial crime typologies, embedded into bank compliance workflows and tuned to jurisdiction-specific rules; switching costs are high once integrated with core banking and case management systems.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Model performance and data engineering on very high-volume transactional data (latency and cost of scoring every transaction, plus data quality and integration with legacy core banking systems).
Early Majority
Positions itself as an AI-first, model-driven AML platform versus legacy rules-centric systems, emphasizing lower false positives and adaptive learning on evolving financial crime patterns rather than static threshold rules.
146 use cases in this application