Rule-based fraud systems generate 30-70% false positives and miss novel fraud patterns. Global payment fraud losses are projected at $40.6B by 2027. ML-based detection catches 2-4× more genuine threats with 60-85% fewer false positives.
Models trained on labeled fraud history score every payment and transaction in milliseconds against features including amount, counterparty, timing, location, behavior pattern, and network graph context. High-risk items block automatically; medium-risk routes to review; low-risk passes through. Continuous retraining adapts to new fraud patterns.
Real-time scoring engine + supervised + unsupervised ML models + graph database for counterparty networks + investigation workflow + human-in-the-loop feedback layer.
Centralized payment factory executing payments across entities and currencies through one hub with real-time rails and in-house bank structures.
The payment factory provides the centralized payment stream that fraud models score in real time.
Continuous AI/ML and LLM-agent scanning of 100% of transactions for anomalies and control failures, replacing sample-based audit testing.
Agentic controls monitoring provides the labeled exception corpus used to train fraud detection models.
Automated matching of purchase order, goods receipt, and supplier invoice to drive touchless invoice processing and eliminate duplicate payments.
AP automation ensures clean PO/GR/invoice data that enriches fraud detection feature sets.
Nothing downstream yet.
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