Think of Hawk AI as a 24/7 digital security team for banks that watches every transaction, compares it to normal behavior, and raises smart, explainable alerts when something looks like money laundering or fraud.
Traditional rule-based anti–money laundering (AML) and fraud systems generate huge volumes of false positives, miss sophisticated criminal patterns, and require large manual investigation teams. Hawk AI aims to cut false positives, catch more real suspicious activity, and make compliance teams far more efficient and auditable.
Domain-specialized AML/fincrime models trained on financial patterns, integrated into regulator-facing workflows and bank-grade infrastructure; switching costs are high once deeply embedded in core banking and compliance processes.
Hybrid
Structured SQL
High (Custom Models/Infra)
High-throughput, low-latency scoring over streaming transactional data while maintaining strict auditability and data residency/compliance constraints.
Early Majority
Positions itself as an AI-first, cloud-native AML and fraud platform with explainable models, aiming to be more accurate and cost-efficient than legacy rule-based and on-premise systems while remaining regulator-friendly.
146 use cases in this application