Aurionpro has launched Fintra, an AI-native trade finance platform where AI agents gradually “earn” autonomy through performance, rather than operating with pre-built decision authority.
Fintra tackles inefficiencies in global trade finance – such as high document rejection rates – by using structured data, continuous learning, and a confidence-gated system that routes uncertain or high-risk decisions to human oversight.
Targeting banks in India, the Middle East, and South Asia, Fintra improves speed and efficiency while maintaining governance.

Global enterprise technology company Aurionpro has launched Fintra: an AI-native trade finance platform where AI agents progressively earn autonomy, rather than having it pre-embedded in the system. 

Global trade finance is valued at over $10 trillion annually, and according to the World Trade Organization (WTO), AI is expected to boost this number by nearly 40% by 2040. 

However, the International Chamber of Commerce (ICC) estimates a 70% rejection rate on first presentation of trade finance documents, and Aurionpro attributes this largely to the legacy infrastructure driving trade finance. 

According to Aurionpro, AI that has pre-existing autonomy isn’t compatible with an underlying architecture that anticipates human-led decisionmaking.

Trade Finance Global (TFG) heard from Janbal Dharmaraj, Senior Manager Corporate Communications at Aurionpro. 

“Fintra avoids legacy constraints by designing its architecture as AI-native rather than retrofitting automation onto human-centric workflows. It does not mimic human keystrokes or blindly follow a decision algorithm; it goes by structured data models, contextual reasoning, and continuous learning loops,” explained Dharmaraj. 

“Confidence-gated handoff protocol (CGHP) provides a governed runtime that evaluates every decision before routing. Every action is logged, and decision pathways are fully auditable, which means the system never pretends a machine decision carries the same contextual judgment as a human one: it simply routes decisions to the right actor based on real-time confidence data.”

Fintra, which primarily targets banks across India, the Middle East, and South Asia, operates under a copilot model that enables human governance at each decision point.

A whitepaper by Ashish Rai, Group CEO at Aurionpro Solution, highlights how Fintra’s AI agents process letters of credit (LCs), bank guarantees, and documentary collections. They analyse risk and impact, while providing real-time facility management, SWIFT message generation, and general ledger (GL) accounting. 

Each automated process is audited by the CGHP, which evaluates every AI decision through four dimensions. These dimensions include: 

Confidence score: examining whether the agent is certain

Materiality: questioning how much money is at stake

Regulatory mandate: checking if there is a legal requirement for a human sign-off

Novelty: observing whether the same pattern has surfaced previously. 

For AI to operate autonomously, it needs to pass all four criteria, and any failure reroutes the process to a human. 

The system tracks its agreement rates. It is only when the data reflects a 95% agreement rate, sustained over months on a particular decision, that the bank allows for automated approvals. 

Fintra’s model is an industry first to the extent that “no one in the industry has operationalised this so concretely,” Dharmaraj explained. “The approach is definitely novel in its specificity to trade finance, though not entirely without precedent in AI broadly. Other players have bits and pieces; Fintra is the first to bring it together with real governance built in.”

A key asset of AI in trade finance is its efficiency gains. Document extraction once took over 10 minutes and can now be reduced to a few seconds. Even with mandatory human checkpoints, the time freed is significant.

“The CGHP routes decisions to a banker only when confidence, materiality, regulatory compliance, or pre-defined thresholds are not met, meaning the vast majority of routine decisions flow automatically,” said Dharmaraj. “Over time, as the system earns autonomy, efficiency will only compound, potentially surpassing competitors who may face setbacks from over-automation without sufficient safeguards.”