In recent years, there has been a lot of discussion and excitement surrounding Generative Artificial Intelligence (GenAI) and its potential to revolutionize various industries. GenAI, which includes technologies like large language models (LLMs), has already made a significant impact in fields like education, coding, and research. However, there are also concerns about the potential risks and challenges associated with GenAI, such as privacy breaches and the spread of misinformation.
When it comes to trade finance, there have been both successful implementations of GenAI and instances where the hype has fallen short. The capacity of LLMs to process massive amounts of data and generate human-like language has the potential to bring significant benefits to the trade finance space. However, caution and careful implementation are necessary to navigate the complexities and challenges that arise.
One important aspect to understand is the distinction between LLMs and GenAI. While GenAI includes various AI techniques that can generate content beyond just language, LLMs specifically focus on generating and understanding human language. These language models are complex systems that function similarly to the human brain, with interconnected nodes representing concepts and information.
In trade finance, the industry has been working towards digitizing processes and reducing reliance on paper documents. While progress has been made, most banks still process PDF images of documents. GenAI can assist in extracting data accurately from these images, including complex tables and handwritten information. With continuous model uptraining, GenAI can achieve high success rates in data extraction, even for non-standardized documents like invoices.
LLMs also have the potential to transform education in trade finance. They can speed up tasks like assessing potential investors or onboarding suppliers by inputting prompts to a GenAI frontend. Additionally, LLMs can provide reactive glossary-type services, offering relevant information throughout the transaction process. As trade finance operators transition to more decision-making roles, LLMs can assist in automating narrative generation for discrepancies and financial crime risks, facilitating on-the-job learning.
Compliance in trade finance is another area where GenAI can make a significant impact. Risk assessment, entity resolution, and identification of dual-use goods can be revolutionized by GenAI’s ability to search and distill information from vast sources. Difficult processes like KYC/KYS checks and AML red flag assessments can be streamlined and informed with the application of these techniques.
However, it’s important to note that GenAI still has limitations and risks. Privacy, ethics, and bias are concerns that need to be addressed through proper oversight and regulation. For example, GenAI is not yet capable of accurately automating the checking of documents under the Uniform Customs and Practice for Documentary Credits (UCP) 600. Machines struggle to distinguish between established rules and customary practices, highlighting the challenges of teaching machines “common sense” and self-awareness.
Despite the constraints and challenges, there are numerous use cases for GenAI and LLMs in trade finance, with the potential for significant transformation. The ability of technology to empower and assist human activity is exciting, and a future where bots handle mundane tasks with minimal error rates while humans learn from AI and make decisions is within reach.
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