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 the data and orchestration layers. A Policy Store governs every AI operation through versioned, tenant-scoped rules; a Knowledge Graph models relationships between entities and documents for real-time validation; and a Multi-Agent Orchestrator executes tasks under policy gates while emitting immutable, cryptographically verifiable audit events. This paper formalises the architecture, regulatory alignment, and novel technical contributions that together establish a defensible framework for safe AI deployment in regulated financial systems. Unlike document automation systems, TradeQu fuses policy-aware orchestration, graph-based reasoning, and multi-agent governance into a single infrastructure layer.