TradeQu has released Policy-Aware Multi-Agent AI Infrastructure for Trade Finance Compliance (v1.7), a research paper defining a new architectural framework for trustworthy and explainable AI in regulated finance.
The publication formalises TradeQu’s approach to embedding compliance and governance directly into the infrastructure layer of AI systems, treating regulation as an engineering principle rather than an external control.
Published on Zenodo (DOI: 10.5281/zenodo.17440451), the paper establishes prior art for compliance-as-infrastructure in high-assurance domains such as trade finance and regulatory technology.
Key Contributions
The research introduces five architectural components that enable explainable and auditable AI operations:
Policy-Aware Orchestration — runtime enforcement of policies before any model or tool invocation.
Graph-Augmented Compliance Validation — reasoning over entities, relationships, and trade-finance documents mapped to UCP 600 and MLETR.
Cryptographically Verifiable Audit Trails — sub-30-second verification using tamper-evident event signatures.
Multi-Layer Defence-in-Depth — unified controls across tenant isolation, policy enforcement, and audit verification.
Domain-Adaptive Entity Recognition — policy-gated language-model reasoning for trade-finance terminology.
Closing the Governance Gap
“AI adoption in trade finance is limited less by model performance than by the gap between regulatory expectations and technical implementation,”
“Our work treats compliance as infrastructure—ensuring every AI operation is explainable, auditable, and regulation-aligned by design.”
— Sam Carter, founder of TradeQu and author of the paper.
Research and Development
The paper serves as a defensive disclosure, documenting the integration of policy-as-code enforcement, regulatory knowledge-graph reasoning, and multi-agent orchestration.
TradeQu is now advancing this architecture toward pilot programs planned for Q1–Q2 2026, in collaboration with financial institutions to validate the system’s compliance performance under real-world workloads.
Access the Research
📄 DOI: 10.5281/zenodo.17434278 (latest version)
📜 License: CC BY-NC 4.0
✉️ Contact: research@tradequ.ai
About TradeQu
TradeQu builds AI-native infrastructure for regulated trade finance. Its architecture enables explainable, policy-aware systems compliant with UCP 600, MLETR, and the EU AI Act—bridging the gap between regulation and intelligent automation.
We’re always looking for collaborators exploring how intelligence can become verifiable.
Let’s build the future of compliant AI together.
If your institution is exploring AI governance, policy-as-code, or explainable infrastructure, we’d like to collaborate.