Partnership: The power of three in trade finance

Trade finance, ill-famed for its paper-heavy processes and regulatory hurdles, is being actively transformed through collaboration between banks, fintechs, and tech innovators.

Finastra, known for its leading portfolio of financial services software, has been prominent in spearheading this technological transformation. Since 2010, it has partnered with Lloyds, providing them with trade finance platform technology; and its long-term relationship evolved following Lloyds’ partnership with Cleareye.ai, an artificial intelligence (AI) platform for compliance. 

Trade digitalisation is a story of partnership: between various types of intelligence, human or otherwise; between financial institutions and fintechs; between fintechs of all sizes and technology platforms; and all manner of things in between.

The biggest hurdles to efficient trade

“I think in the documentary trade space there are two key challenges that we face,” said Jon Boran, Head of Future Trade Product at Lloyds, in an exclusive Finastra webinar. “One is the paper problem, and the other is skills.”

Despite innovative technology and the legal enablers that allow this technology to be harnessed, paper continues to dominate trade. In 2023, the same year as when the UK’s Electronic Trade Documents Act (ETDA) was introduced, global trade depended on four billion paper documents daily. Beyond wastefulness, paper reliance adds unnecessary complexity and operational risk. 

The ETDA legally recognises negotiable trade documents, such as bills of lading (BLs), in electronic form. It was adopted as a means to save on costs and increase efficiency. The International Chamber of Commerce (ICC) projected that by 2024, digitising trade documents could generate up to £25 billion in economic growth and £224 billion in efficiency savings.  

“The solution is digital trade,” said Boran. “Harnessing the ETDA to allow for documents to move seamlessly between parties at a much quicker rate and avoid the bottlenecks that we see in paper.”

Boran highlighted a “skills gap” as the second key obstacle to efficient trade. Examining documents under the Uniform Customs and Practice for Documentary Credits (UCP) and the International Standard Banking Practice (ISBP) is considered a high-skill role. These processes ensure that banks follow a standardised framework in banking, safeguarding consistency and reducing risk in cross-border trade. 

“It’s a role that requires years of training to be able to successfully check documents,” explained Boran. Fewer people are moving into niche roles: industry veterans are retiring, which is compounded by the fact that those entering the workforce today want broader experience in banking. 

Lloyds has chosen to fill skills gaps through automation. For Boran, the key is to get machines to do the ordinary, so that people can do the extraordinary. 

Automation in banking

Fintech companies help banks manage these burdensome, paper-intensive trade processes. Through automation, banks can provide a comprehensive, integrated system service for trade counterparts. Trade documents are unstructured data, making them time-intensive to approach manually. The three musketeers thereby work together to tame the mayhem that is unstructured data, as they restore order in the world of trade. 

During the webinar, Andrew Dalton, Vice President of Sales at Cleareye.ai, described their work through three pillars:

1. Documents are first digitised by putting them onto their platform
2. Then, AI is permitted to read the document and extract the necessary information
3. Finally, various screening processes are applied to perform trade document checking, compliance checking, and trade-based money laundering (TBML) screening.

According to Boran, Lloyds has already seen an outcome of 40% in efficiency savings in relation to average transactions across two key documentary trade flows from this Cleareye.ai collaboration. There is work to be done, but the intention is that this initiative will grow to support risk reduction and standardisation.  

Even though the trade finance business is highly standardised and regulated, operational processes vary for every bank. 

“The process within each financial institution is unique,” said Dalton, emphasising the need for flexibility. The technology exists – it just needs to be correctly allocated, and we should find a golden middle between heavy customisation and generic solutions that would help the industry to move faster. 

A shift in mindsets

The ability of different systems to work together remains a significant challenge to effective integration between technological and financial innovation. 

“This is the biggest change we have ever seen for our colleagues,” Boran said. “It’s a complete mindset change, from checking documents in front of you on paper or on the screen, to doing an extraction process.” 

Boran elaborated on their approach, which involved internal sessions with staff, alongside one-on-one meetings with the Cleareye.ai team. 

Having an integrated solution in place is key. “It’s all well and good having tools, people, and fragmented processes, but when you look at the full journey – that’s when the magic really happens,” said Dalton.

Another crucial approach for Dalton is transparency. Being open with their clients on how they work, and sharing out-of-the-box capabilities that can be channelled through innovation, allows clients to stay agile in the rapidly evolving world of fintech.

Trade finance endgame: Continuing to innovate

“What we are looking at now is how we can put structured data into the document, so that it can be machine-read, rather than being read via optical character recognition (OCR),” said Boran.

OCR is the technology that uses automated data extraction to turn it into a format readable by machines. Structured data refers to data that holds a fixed schema, and it enables a document to be standardised from the outset. It allows documents to be readable by AI, eliminates the need for manual intervention, and ultimately allows banks to provide an end-to-end service. 

With AI applied on top of structured data, they were able to extract the entire embedded data, rather than the 90% that is expected from OCR processes on top of unstructured data. “That’s a saving,” Boran said. “Doesn’t sound like a lot – 90 to 100% – but if you add that up with all of the documentation and the data points, it creates a big efficiency.”

By enabling automated extraction and interpretation of information, this lays the foundation for fully digital and streamlined trade document checking, fraud detection, and TBML screening as the industry moves toward digital transactions and digital native documents. 

Until that becomes the norm, organisations must continue working with a mix of structured and unstructured data native documents. Until that becomes the norm, organisations must continue working with a mix of structured and unstructured data-native documents. Until that becomes the norm, organisations must continue working with a mix of structured and unstructured data.

Although the next mission for the team, embedding structured data into documents, will take a while, it is something that Boran expects to happen more and more, especially as the industry approaches the ICC targets for digitisation. 

Sponsored by Finastra