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What are Large Language Models?
Large Language Models (LLMs), such as OpenAI’s GPT-4, are advanced AI systems trained on extensive text datasets. These models excel in understanding and generating human-like text, making them ideal for tasks involving language comprehension and generation.
When applied to the right use cases, LLMs can generate significant value, particularly in trade finance, by enhancing document checking and regulatory compliance.
However, the true potential of LLMs lies in their judicious application. Identifying the appropriate use cases is key to leveraging their strengths effectively.
By focusing on specific problems that need solving, businesses can ensure that technology serves their needs, rather than the other way around. Sometimes, simpler technologies may suffice, but for more complex issues, LLMs provide unparalleled capabilities.
Prioritising problem solving over technology
In the context of trade finance, the emphasis should always be on solving the problem at hand. For example, a rule-based system might suffice for validating shipping documents against regulatory standards, rendering an LLM or generative AI (Gen AI) solution unnecessary. This distinction is crucial because implementing AI solutions in a business environment requires careful consideration of various factors, unlike casual experimentation with AI tools.
Implementation considerations and adoption of LLMs and Gen AI
Deciding whether to host an LLM on-premises or use cloud infrastructure involves evaluating costs and benefits. Enterprises need to consider the initial setup costs, ongoing maintenance, and scalability potential against the efficiency gains in document checking and compliance.
Once the decision is made to use LLMs or Gen AI for a specific use case, businesses have several implementation options. Each approach has its own set of advantages and disadvantages that need careful evaluation.
One option is to integrate a closed model with a commercial API. The primary advantage of this approach is the ability to rapidly prototype and deploy the solution. Businesses can quickly set up applications and see immediate results.
However, there are notable disadvantages. Potential issues with data security and latency can arise, and there is a dependency on the provider’s terms of usage and service level agreements (SLAs). This reliance on an external provider can limit scalability and long-term flexibility, as changes in the provider’s policies can directly impact the business’s operations.
Alternatively, businesses can choose to host an open model on cloud infrastructure. This option offers full control over the model and its deployment, providing greater flexibility in customising and scaling the solution. However, hosting an open model comes with its own set of challenges.
Limited insight into the initial training data often raises concerns for customers’ model governance teams. Additionally, maintaining and operating the model requires a significant investment in infrastructure. Businesses must carefully consider whether the infrastructure costs are justified by the efficiency gains provided by the use case.
Role of LLMs in trade finance
LLMs can be transformative in trade finance, particularly in document checking and compliance checks. Key use cases include automated data extraction and validation, compliance checking, natural language understanding, and sanctions screening. A few use cases to consider.
- Automated data extraction and validation
LLMs can extract critical information from trade documents efficiently. For instance, they can identify and extract data fields from a Letter of Credit (LC), such as the beneficiary’s name, amount, expiration date, etc.
Once extracted, the LLM can validate this information against other related documents to ensure consistency and compliance. Similarly, LLMs can run consistency checks across document types. For example, identify mismatched dates or incorrect product descriptions between an invoice and a bill of lading. LLMs can also suggest corrections or flag documents for further human review.
- Compliance checking
Trade finance involves adherence to international regulations and standards, such as the Uniform Customs and Practice for Documentary Credits (UCP 600). LLMs can be programmed to check documents for compliance with these standards by cross-referencing the content with the regulatory requirements.
- Natural language understanding
LLMs are adept at understanding the context and nuances of language. This capability is crucial in trade finance, where the language used in documents can be complex and varied. LLMs can interpret clauses from 46/47 tags of an LC, comparing against the documents presented, making it easier to identify issues that may not be immediately obvious.
- Sanctions, trade based money laundering checks
LLMs can accurately identify and extract nouns for sanction screening, such as company names, product names, geographical locations, financial terms, and other critical entities from documents.
For example, in a bill of lading, LLMs can isolate terms like “consignee,” “shipper,” “port of loading,” and “port of discharge. By understanding the context in which these nouns are used, LLMs ensure that the extracted information is relevant and accurate, facilitating seamless integration with sanction screening systems.
Onerous clauses are provisions in contracts or trade documents that impose heavy obligations or burdens on one party. LLMs can identify and flag onerous clauses by analysing the language and comparing it with a database of known burdensome terms and conditions.
Similarly, Risk Assessment LLMs can provide an assessment of the risk associated with these clauses, enabling companies to negotiate better terms or seek legal advice before proceeding with the trade.
Anti-boycott clauses are provisions that prevent parties from participating in or complying with boycotts not sanctioned by their home country’s government. LLMs can detect and highlight anti-boycott clauses within trade documents by identifying specific keywords and phrases commonly associated with such provisions, thereby reducing the risk of legal penalties and ensuring ethical business practices.
- Guarantee/SBLC creation (drafting tool)
The use of LLMs has the potential to revolutionise various processes, including the creation of Standby Letters of Credit (SBLCs) and guarantees.
These financial instruments are critical for securing transactions and providing assurance to parties involved in international trade. An LLM-powered guarantee/SBLC creation tool can streamline the drafting process, ensuring accuracy, compliance, and efficiency.
The Guarantee/SBLC creation tool leverages LLMs to generate guarantee texts based on predefined input criteria. This tool can be configured to align with a bank’s acceptable templates and clauses, ensuring that the generated documents meet institutional standards and regulatory requirements.
- Vetting of guarantee text
The vetting of guarantee texts is a crucial aspect of trade finance, ensuring that all documents meet legal, regulatory, and institutional standards. LLMs can be employed to automate and enhance the vetting process, providing a thorough analysis of guarantee texts.
This includes identifying onerous clauses, pinpointing inconsistencies, assessing workability issues, comparing documents to bank templates, and creating MT760 messages from client applications.
The vetting tool, powered by LLMs, offers a comprehensive solution for analyzing guarantee texts. By leveraging advanced natural language processing capabilities, the tool can scrutinise documents for potential risks and ensure alignment with bank-approved templates. This ensures that guarantees are both legally sound and operationally feasible.
Benefits of using LLMs in document checking
- Efficiency: Automating the document checking process significantly reduces the time required to verify documents, allowing for faster trade transactions.
- Accuracy: LLMs minimise human errors and improve the accuracy of document verification.
- Cost-Effectiveness: Reducing manual efforts and operational costs for financial institutions and trading companies.
Challenges and considerations
Despite their advantages, LLMs also pose some challenges:
- Data privacy: Ensuring the confidentiality of sensitive trade information is paramount.
- Model training: LLMs require extensive training data specific to trade finance, which can be resource-intensive.
- Human oversight: While LLMs can automate many tasks, human oversight remains necessary to handle complex cases and ensure the final decision’s accuracy.
While LLMs and generative AI offer immense potential, their implementation should be guided by a clear understanding of the use case, problem-solving priorities, and a balanced evaluation of the available options and their implications.
