Policy-Aware Multi-Agent AI Infrastructure for Trade Finance Compliance

Errors or delays in trade-finance document verification can lock up billions in working capital and expose banks to compliance penalties exceeding US$1 million per incident. Trade finance involves complex, high-value transactions governed by overlapping regulatory frameworks, including UCP 600, AML/KYC, Basel III, and the EU AI Act. Existing digitalisation efforts focus on document automation or post-hoc compliance rather than infrastructure that enforces governance at runtime. TradeQu introduces a policy-aware, graph-augmented, multi-agent infrastructure that embeds compliance logic directly into data and orchestration layers. A Policy Store governs AI operations through versioned, tenant-scoped rules; a Knowledge Graph models relationships for real-time validation; and a Multi-Agent Orchestrator executes tasks under policy gates while emitting cryptographically verifiable audit events. In staged tests on commodity cloud infrastructure, policy evaluation averaged 2.3 ms per request at 500 TPS, and graph traversals completed under 25 ms on 10,000-node graphs. The system detected duplicate Bills of Lading across Letters of Credit that SQL joins missed, demonstrating the advantages of relationship-first reasoning. We formalise five technical contributions: (1) policy-aware AI orchestration with runtime enforcement preceding model invocation, (2) graph-augmented compliance validation mapped to UCP 600 rules, (3) cryptographically verifiable audit trails with sub-30s verification, (4) multi-layer defence-in-depth enforcement with zero cross-tenant leakage across 10,000 requests, and (5) domain-adaptive entity recognition with policy-gated LLM fallback. This defensive publication establishes prior art for integrated policy-graph-agent architectures in regulated finance, demonstrating compliance-as-infrastructure that aligns with EU AI Act requirements for high-risk systems and enables safe AI deployment in trade finance.