Community

From the point of view of Trade Finance, 

I think the reason, for the low % of return on huge investment in across all the financial institution associated with trade finance, is following

1. Regulatory fragmantation across jurisdictions with varying compliance requirements, especially with rising tariffs and sanctions. AI systems struggle tLackoo adapt dynamically to constantly evolving regulations and geopolitical shifts. This results in delays in cross-border transactions and increased risk of non-compliance
2. Since, the nternational trade involves unfamiliar stakeholders, making trust a major concern. AI lacks the human judgment needed to assess nuanced risks or reputational factors. Letters of Credit (LCs) still rely heavily on manual verification due to trust concerns.
3. Many banks and corporates still use paper documentation for LCs, invoices, and customs. AI can digitize and analyze documents, but full automation is hindered by lack of standardized formats and interoperability. Bottlenecks in document examination and fraud detection persists.
4. Trade data is fragmented across platforms, banks, and customs authorities. Without harmonized data standards, AI cannot fully optimize workflows or risk models. Inconsistent data leads to errors, delays, and poor decision-making.
5. Since, AI is still to overcome some of the "human" properties, AI adoption requires a hybrid model—machines for speed, humans for judgment. Over-reliance on AI can lead to blind spots in compliance, fraud detection, and customer service. Resistance to change and lack of skilled personnel slow down transformation.

There are some solutions on horizon, such as
1. Interoperable digital platforms using APIs and blockchain to unify trade data.
2. AI-powered discrepancy detection for LCs and invoices, reducing manual errors.
3. Global regulatory alignment efforts like MLETR to standardize digital trade instruments