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How LLMs are transforming trade finance operations

As global trade grows in complexity, banks are turning to advanced automation for greater speed and accuracy. Chandrasekhar Somasekhar, chief technology officer, and Denise Collaku, senior vice-president at Cleareye.ai, explore how large language models (LLMs) are reshaping trade finance operations and compliance.

Trade finance is undergoing a seismic shift. As global trade volumes rise and regulatory demands intensify, financial institutions must streamline operations without compromising compliance or accuracy. We believe that at the heart of this transformation lies an emerging force: Cleareye.ai’s automated solutions, powered by advanced LLMs.

Transforming trade finance with LLMs

The complexity of trade documents requires more than basic automation; advanced solutions are needed to ensure accurate document examination and compliance checks. Whether it’s unstructured bill of lading texts, multi-page letter of credit presentations or compliance-heavy invoices, LLMs can now read, interpret and extract insights at unprecedented speed and accuracy. Cleareye.ai’s intelligent automation platform leverages cutting-edge LLMs to tackle some of the industry’s biggest challenges, which include document complexity and data fragmentation. Unlike traditional OCR or rule-based systems, Cleareye.ai’s LLM-powered platform goes beyond surface-level text recognition. It understands context, identifies nuances in document language and ensures compliance checks align with evolving global regulations. Leveraging this advanced intelligence, Cleareye.ai’s platform minimises discrepancies, shortens turnaround times and boosts overall operational efficiency, bringing banks closer to real-time trade processing.

Smarter compliance, lower risk

Beyond document processing, with the help of advanced models, Cleareye.ai’s platform is also redefining the scope of compliance and risk management. This includes areas like trade-based money laundering (TBML) detection, where checks are critically important due to the unique vulnerabilities and complexities of international trade transactions. Financial institutions are subject to stringent anti-money laundering (AML) regulations to detect and prevent illicit financial flows hidden in trade transactions, and to ensure alignment with internal governance policies and risk appetite frameworks. Cleareye.ai’s advanced models analyse language patterns across documents, identify inconsistencies and flag suspicious activities, strengthening compliance while minimising false positives. The platform incorporates International Chamber of Commerce rules and enables comprehensive vessel tracking, adverse media screening, boycott language detection, over- and under- invoicing analysis and line-of-business validations against goods shipped to generate an overall transaction risk profile. It also performs additional checks for credit exposures, duplicate invoices, landlocked country transactions and bills of lading. This empowers trade operations teams to make informed, compliant and confident decisions.

Chandrasekhar Somasekhar 3

Chandrasekhar Somasekhar

Chief Technology Officer

Chandrasekhar Somasekhar, Cleareye.ai

“The future of trade finance won’t be built on paper; it’ll be built on intelligence. Cleareye.ai is helping banks take the first real steps toward that future, using LLMs to turn complexity into actionable insight.”

Collaku Denise Headshot 1 1

Denise Collaku

Senior Vice President Customer Success

Rethinking guarantees and SBLCs

While automation powered by LLMs is transforming trade document handling, guarantees and standby letters of credit (SBLCs) remain a significant opportunity for transformation. These instruments are vital in managing risk, but the workflows behind them remain largely manual, paper-heavy and compliance-intensive. Cleareye.ai is reimagining how banks issue and manage guarantees and SBLCs by bringing speed, transparency and control to processes that have long been bogged down by inefficiency.

Challenges in today’s guarantee and SBLC workflows include:

Manual, paper-heavy processes that slow execution

Disjointed compliance checks for sanctions and TBML

Limited visibility among stakeholders

High operational costs in increasingly low-margin environments

Artificial intelligence, including LLMs and natural language processing, helps modernise these workflows end-to-end by:

Digitising and extracting data from unstructured formats

Screening counterparties in real time against sanctions and AML lists

Automating document validation for completeness and compliance

Generating reports and audit trails to support regulatory readiness

This intelligent orchestration allows banks to issue guarantees and SBLCs faster, strengthen compliance and enhance customer experience, all while reducing operational risk and cost.

“Guarantees and SBLCs have long been stuck in manual, paper-heavy workflows,” says Denise Collaku, senior vice-president at Cleareye.ai.

“Cleareye.ai is changing that by bringing intelligent automation to the forefront so banks can issue, manage and monitor these instruments with speed, transparency and confidence.”

Driving results that matter

By combining the power of LLMs in document intelligence with automated SBLC and guarantee processing, Cleareye.ai is helping banks future-proof their trade finance operations. The result? A faster, smarter, and more compliant trade ecosystem that is built on trust, transparency, and technology:

  • Up to 40% reduction in processing time, depending on document complexity
  • Fewer false positives, less rework, and faster resolution
  • Built-in audit trails for every step
  • Easily integrates with core banking and trade finance platforms

The responsible path to LLM adoption

The true value of LLMs lies in their judicious application. Identifying 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. In some instances, simpler technologies may suffice, but for more complex issues, LLMs provide unparalleled capabilities. At Cleareye.ai, we work with banks that are open to exploring Gen AI and LLM-based approaches, providing the option to leverage a closed-source multimodal LLM for information retrieval and interpretation for mutually agreeable scenarios. This enhances user productivity while maintaining transparency.

100% LLM approach and its challenges

A fully LLM-based approach offers promise but also presents several challenges:

Data privacy

Ensuring the confidentiality of sensitive trade information is paramount. Robust data governance is essential to ensure regulatory compliance and protect confidential data.

Model training

LLMs require extensive training data specific to trade finance, which can be computationally intensive and demands careful planning and infrastructure investment.

Human oversight

While LLMs can automate many tasks, human oversight remains necessary to handle complex cases and ensure the accuracy of the final decision.

Cost considerations

Compared to traditional machine learning approaches, a 100% LLM approach can be two or three times more expensive. Maintaining and operating the model requires a significant investment in infrastructure and may not justify the return on investment.

By balancing innovation with prudence, Cleareye.ai ensures banks can leverage LLMs responsibly, thus driving efficiency, improving compliance and maintaining transparency, all while ensuring that technology serves business priorities and regulatory integrity.
“The future of trade finance won’t be built on paper; it’ll be built on intelligence. Cleareye.ai is helping banks take the first real steps toward that future, using LLMs to turn complexity into actionable insight,” says Chandrasekhar Somasekhar, Cleareye.ai’s chief technology officer.

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