Harnessing GenAI in Trade Finance: A Leap Towards Efficient Data Management and Enhanced Compliance

In the fast-paced world of trade finance, managing and scrutinizing the unstructured data involved in transactions can be a daunting task. Traditional methods, burdened by the need for extensive manual effort and the potential for human error, struggle to keep pace with the complexities and regulatory demands. However, with the advent of Generative AI (GenAI) technologies, the industry is witnessing a transformative shift. This blog explores how GenAI is revolutionizing the digitization process, enhancing accuracy, and streamlining compliance in trade finance.

Digitization of Unstructured Data:?

In the current trade finance landscape, transactions often involve a myriad of unstructured documents. Banks face significant challenges in manually scrutinizing these documents to ensure international regulations like UCP/ISBP, URC etc., compliance checks, performing consistency checks and sanctions screening. This process, typically handled by various personas within a bank, is time-consuming and prone to errors.

One of the initial steps towards automating this scrutinization process is digitizing unstructured documents. This involves classifying documents into corresponding types, categorizing them as originals or copies, and generating key-value pairs from each document. Traditional machine learning models pose challenges such as frequent retraining requirements, extensive training durations, and the need for large sample collections, especially for uncommon trade documents.

Embracing the latest GenAI capabilities can significantly simplify this process. GenAI models, trained on several billion parameters, can deliver near-accurate results. FinTech companies like Cleareye.ai, a leading solution provider in the Trade Finance space, leverage GenAI to correctly classify various documents in trade transactions and extract relevant information, transforming it into an internal standard data structure. These context-aware predictions utilize private data and APIs through methods like Retrieval-Augmented Generation (RAG) or simple chains.

This approach reduces the manual effort required for training new documents or formats, which can take weeks to months, and minimizes the risk of human errors during training data preparation. One notable use case for GenAI is interpreting the 46A/47A clauses in a Letter of Credit. Additionally, GenAI can identify different goods involved in a transaction from the 45A tag (a free text) and correlate details such as unit price, purchase order numbers, HSCode, etc. Maintaining this information in a structured format facilitates the automation of scrutiny and compliance processes.

Combining existing learning systems with GenAI enhances document examination and consistency checks. Complex international trade rules, when prompted correctly against context-aware inputs, yield better results compared to traditional rule engines. Scenarios like identifying landlocked countries or cities, port country matches, handling exceptions in documents or Letters of Credit (LC), flagging anti-boycott clauses, and detecting overdrawn or underdrawn transactions can be addressed with high accuracy and minimal effort using GenAI. This list of verifications, though extensive, represents only a fraction of the capabilities of large models.

Advanced Compliance Checks:

Compliance verification is a critical aspect of any trade finance transaction. This involves tracking vessels to identify illegal ports, monitoring high-risk countries, checking for dual-use goods, performing fair price checks, and conducting sanction screenings. Integrations with third-party services enhance the compliance check process. Often, customization requirements arise, allowing certain countries to trade with specific partners while others may be blacklisted.

GenAI aids in accurately identifying goods, their composition, and the possibility of dual use, reducing the time required for verification. For instance, when LC mentions generic goods, GenAI can precisely categorize the actual goods listed in the invoice. Additionally, GenAI models can fetch the latest market prices for goods, providing insights into fair pricing and alerting banks to potential fraudulent activities. These models also help identify the line of business for key customers, further safeguarding financial transactions.

Additional GenAI insights:

• Provide a summary of the transaction with the parties involved, amount, goods, source, destination, etc.
• Monitor media coverage to gauge the reputation of parties involved in transactions.
• Forecast market trends to pre-emptively address potential price fluctuations.
• Identify supply chain disruptions early, mitigating delays in manufacturing or shipping.
• Detect and prevent fraudulent activities by analyzing historical transaction patterns.

The integration of GenAI into the trade finance ecosystem marks a significant leap forward in managing unstructured data and ensuring compliance. By automating complex processes and providing context-aware insights, GenAI not only reduces the manual burden but also enhances accuracy and efficiency. As financial institutions continue to embrace these advanced capabilities, the future of trade finance looks promising, characterized by greater transparency, reduced risk, and a more seamless transaction process. Embracing GenAI is not just a technological upgrade; it is a strategic move towards a smarter, more resilient financial world.

Article by; Anoop Nair, Associate Director – Product Engineering, Cleareye.ai

Read the article on Cleareye's website here.