Standby Letters of Credit (SBLCs) are quite crucial in trade finance. They are backed by banks and act as a safety measure to meet financial obligations. However, SBLCs also expose banks to risks. SBLC risk hides in dense, unstructured wording and is often found too late, creating rework, delays and friction for the bank and its customer.
Missing clauses, conflicting terms, and incomplete data often arise during manual reviews after negotiations are complete. Clause-related risks can also be overlooked at the initiation stage, when correction costs are lowest, and the chance to prevent future issues is greatest.
Banks across regions face the same challenge – guarantee and SBLC wording that requires expert judgment to evaluate, but arrives in volumes that make line-by-line reading unsustainable.
To address this, Cleareye.ai has launched its ClearTrade Guarantees and SBLC product, a platform designed to identify risks in guarantee and standby letter of credit wording before issuance.
The product uses agentic AI to assess clause-level acceptability, verify application data against instrument text, and manage version control throughout the amendment process.
It also accommodates regional regulation, applicable rule sets, wording standards and bank-specific approval policies, recognising that the underlying problem transcends geography even as the regulatory context varies.
What does the product do?
The ClearTrade Guarantees and SBLC product vets guarantees and SBLC wording before issuance and carries it through to amendment. The system evaluates each clause for acceptability, with clear rationale and any identified gaps, conflicts or missing information. The instrument text and the application form are validated against each other and against URDG 758 and ISP98 standards before issuance.
Banks can configure their own approved clause repository, prioritising internal standards over AI suggestions, or use the repository as is.
Users have full control, allowing them to accept, edit, add, or delete wording, rerun checks, and keep a complete version and audit history.
Pre-issuance vetting, issuance, and amendments are connected. Vetted wording carries forward, amendments merge automatically, and name screening alongside trade-based money laundering checks remains within the same workflow.
Benefits for banks: Earlier detection, faster cycles
Timing and consistency are key benefits for banks. Risks such as missing clauses, conflicting terms, and incomplete data surface during pre-issuance vetting, enabling banks to address issues before guarantees or SBLCs are finalised.
Key data is automatically extracted and compared, which replaces traditional line-by-line reading and speeds up review times, allowing banks to handle larger volumes without losing accuracy.
A configurable clause repository ensures consistent use of approved language in all documents. Banks can prioritise their internal standards over AI suggestions, improving governance and reducing variability. The product lowers customer friction.
Consistent approved wording flows from a configurable clause repository. Configurable maker-checker workflows operate at transaction or work item level, and every edit, override, rationale and approval is captured, making the process audit-ready by design.
The system is built to produce a documented trail that a regulator can read, not just a decision that a compliance officer can defend.
How it differs: The system recommends; the user decides
What distinguishes the product is that it separates tasks between the system and the user. The system delivers clear, detailed findings at the clause and field levels. Unclassified text is marked, and the user must confirm any missing data, minimising uncertainty. The user enjoys the authority to accept, edit, or override decisions with a rationale.
Bank-specific governance is embedded in the architecture. Approved clauses can take precedence, and checks align with URDG 758, ISP98 and the bank’s own policy. End-to-end accountability is maintained, with maker-checker control built into the workflow. The system does not replace judgment. It structures the conditions under which judgment is applied.
The implementation of Hybrid AI: Deterministic rules + LLM
The platform behind the product combines fixed rules with large language model-driven intelligence. Cleareye.ai does not believe every trade finance decision should be made by an LLM. High-confidence, repeatable decisions use deterministic logic. Contextual reasoning, interpretation and ambiguity use LLM and agentic AI.
A proprietary Model Gateway serves as an orchestration layer that decides the best execution path from transaction context, task complexity, risk, cost and model availability. The path can be deterministic rules, proprietary models, an LLM, agentic AI, a larger reasoning model or human review. The architecture is model-agnostic, so banks do not have to bet their operational infrastructure on a single LLM provider.
The economic logic is straightforward. Banks use LLMs where they can clearly create value, avoiding the high costs of processing every transaction through a large model. Cleareye.ai positions this as a balance between intelligence, accuracy, latency, risk and cost, rather than treating LLM use as uniform across all decision types.
The company works alongside Technology, Risk, Model Governance, Compliance and Information Security teams to transition LLM and agentic AI from experimentation to production, approaching AI governance as an ongoing journey rather than just a compliance task.
A glance at the deployment and governance
The platform can be deployed on public cloud or private cloud infrastructure, chosen on the basis of data sovereignty, operational resilience and regulatory requirements, without compromising the user experience. The product is designed for Guarantee Specialists, alongside Product, Technology, Compliance, Risk and Corporate Banking leadership.
The product offers a particular capability – automated clause-level review to identify risks before finalising wording. It ensures user control over decisions and creates an audit trail that complies with internal governance and external standards.
