Sovereign Customs & Trade Anomaly Platform
A federal customs authority processing 2.3M annual import declarations suffered from massive port bottlenecks due to a 100% manual inspection rate for high-value shipments. Their rule-based profiling had a low 8% true positive rate for fraud (misdeclaration, undervaluation), causing revenue leakage and slowing down trade.
Architected a multi-model risk scoring system deployed on-premise that identifies anomalies in trade declarations using metadata analysis, NLP, and graph neural networks.
Combined gradient boosting on structured data with GNNs on network data to detect both individual anomalies and systemic fraud networks.
Used Neo4j to map millions of trade relationships, identifying "shells" and known high-risk network patterns that traditional rules miss.
Custom NLP models capable of detecting inconsistencies between declared descriptions and international trade (HS) codes in both Arabic and English.
Built a self-contained retraining pipeline that incorporates officer findings back into the model without any external internet dependency.
Accuracy in identifying actual customs fraud
Reduction in clearing time for low-risk declarations
Recovered from identified underdeclarations in 12 months
Zero data leakage; full on-premise execution
Identifying high-risk hubs in the global trade network that were previously invisible to per-declaration rule systems.
Optimized inference on A100 GPUs to score 2.3M declarations with sub-second latency.
Turning "black box" AI into actionable intelligence with clear, visual evidence for each flagged inspection.
We specialize in building production-grade systems that solve complex operational problems. Let's discuss how we can help architect your solution.