We build fraud detection and AML platforms — real-time transaction monitoring, ML-driven anomaly detection, alert triage, case management, and the regulator-defensible reporting that satisfies the inspection process.
A fraud platform is judged on missed cases and false-positive ratio simultaneously. AML is judged on operational discipline and audit trail. Both have to be excellent; trade-offs between them require explicit choices.
Rules layer for known patterns. ML layer for novel patterns. Network analysis for ring detection. Case management with proper analyst workflow. Regulatory reporting (SAR, CTR, equivalents) generated from the same audit trail.
Detection that misses less; false-positive rate that's tunable; analyst workflow that's defensible; regulatory reporting that doesn't require improvisation.
Sub-100ms decisioning on transaction streams — rules + ML in one decision.
Unsupervised and semi-supervised models for novel-pattern detection; supervised for known fraud typologies.
Graph-based analysis for connected-entity rings, mule networks, and synthetic-identity clusters.
Alert triage, case management, investigation workflow, and outcome capture — feeding back into model training.
Sanctions screening (OFAC, EU, UN), PEP screening, ongoing monitoring, and SAR workflows.
SAR / CTR / equivalent generation, regulator dashboards, and audit-ready exports.