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The trade finance industry is on a modernization journey. Reflecting on 2023, the year ahead will see the digitalization of trade finance take center stage. And it’s about time. With an expected focus on regulation, challenges will be seen across the board as the adoption of AI and ML continues to rise. So what can we expect from 2024?
Digitizing trade has been a topic of conversation for many years, and in the same conversations we often also hear about the widening trade finance gap. As we move through a post-pandemic world, which continues to face significant macro and geopolitical challenges, supply chains are increasingly fragmented and layered in complexity, and a continued reliance on paper remains. Fortunately, we are seeing the “containership” of trade change course, with innovation in trade happening at a faster rate than ever before.
As more regions around the world pass their Model Law on Electronic Transferable Records (MLETR) bills through parliament in 2024, a framework is being put in place to digitize paper documentation. Such bills have the potential to decrease expense, minimize risks and provide real-time trade and settlement, while serving as a foundation for more extensive digitalization efforts.
Yet, trade digitization still poses challenges. This will not be an overnight switch, it will take years to develop a comprehensive and integrated approach that covers the entire trade finance value chain. Note that the Digital Container Shipping Association has set 2030 as its target for electronic bills of lading to reach 100%.
We will see more banks in 2024 choosing platforms that allow them to combine best-of-breed products, a best-practice approach to security and governance, and to offer a cohesive digital customer experience. This helps to de-risk the adoption of new technologies, accelerate their implementation and deployment, and support global production use.
Institutions will accelerate the integration of fintech solutions with their platforms in 2024. For example, applications that use artificial intelligence (AI), machine learning (ML) and real-time data to ‘read’ documents, extract the information required and offer actionable insights. Similarly, there will be increased focus on automating sanctions screening and anti-money laundering (AML) checks. As the mass adoption of generative AI (Gen AI) accelerates, we should expect use cases to include enabling corporates to engage directly with banking systems using natural language to find information, such as working capital solutions that fit their needs. Many of these solutions already exist – banks need to focus on integrating them within an interoperable solution.
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.
David Smith Information Analyst at ManpowerGroup
20 November
Konstantin Rabin Head of Marketing at Kontomatik
19 November
Ruoyu Xie Marketing Manager at Grand Compliance
Seth Perlman Global Head of Product at i2c Inc.
18 November
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