Finance Optimization Automated by ClearDox
By Irina Reitgruber and originally published on CTRMCenter.
Shipments of commodities are typically followed by the preparation of the documents required under a letter of credit (LC) to ensure payment. A number of different documents must be collected, checked for compliance with the LC terms, and then submitted to a bank for payment. These documents may include, among others, a bill of lading, insurance certificate, certificate of origin, and additional supporting documentation. Collecting and reviewing these documents is a complex manual process for those dealing with trade financing. However, modern technology has the potential to significantly improve this workflow.
Recently, I had the opportunity to see a demonstration by ClearDox of an AI-powered solution for automated processing of documentation under letters of credit. What I saw can be summarized as follows.
LCs are automatically ingested, routed into the application, and categorized. Amendments and supporting documents are automatically linked to the master LC record. The application then runs a UCP 600 compliance validation upon upload, evaluating the LC clause by clause and assigning each clause a status of pass, review, or fail, accompanied by a message explaining the assessment. Supporting document requirements are extracted from sections 46A and 47A, with conditions mapped to each document, for example, requirements related to origin, language, or the number of originals and copies.
Automated processes in the solution are based on AI agents – specialized tools trained to perform specific tasks – which can be orchestrated into workflows. According to Marc Lefebvre, the Chief Technology Officer and Co-Founder of ClearDox, the agents as the foundational technology for future AI applications in the commodity and energy sectors, given their flexibility and broad applicability. Back in December 2025, ClearDox described plans to introduce application agents – purpose-built agents embedded within specific applications to deliver structured, targeted automation for particular use cases. The agentic automation within finance optimization is the company’s first application of this approach.
When a supporting document is received, the AI agent automatically compares it against the extracted conditions and generates a validation status together with an explanation. If an amendment is linked to a master LC, the system triggers a full re-evaluation of all previously established statuses and issues new notifications if anything has changed. However, human-in-the-loop approach remains an integral part of system design, ensuring that users retain full control of the process.
All agent actions and user decisions are recorded in an audit log to ensure full transparency. An insights dashboard presents the agents’ findings across applications and highlights their criticality (for example, low or high criticality), allowing users to quickly identify items requiring attention. Notifications and email alerts can be configured to trigger automatically whenever the agent detects a status change.
The AI-driven workflow appears both simple and efficient, while still allowing users to retain all necessary controls. I was wondering how easily this type of automation could be applied to other workflows that typically require significant manual effort.
Katie Carter, Vice President, Product at ClearDox, explained that adapting the solution would require minimal application development. In most cases, it would mainly involve prompt engineering and configuring the agent’s contextual knowledge for a new domain. The underlying workflows, status models, and evaluation logic are largely reusable. ClearDox is working on extension of the process automation to earlier steps in the life cycle – the analysis of the LC based on the sales contract which forms it.
Finally, we discussed the challenge of building trust in automated workflows and addressing the risk of AI agents potentially “hallucinating.” Inconsistency may appear to be a challenge, Katie acknowledged. The same content may sometimes be rated as low risk in one instance and medium risk in another. Even when the result is not technically wrong, this inconsistency can undermine user confidence.
However, there are ways to address this issue. On the one hand, RAG architectures and improvements in vector databases have significantly reduced hallucinations compared with early AI models, Katie explained. “Model maintenance is a part of the proposition and ClearDox ensures robust back testing and frequent upgrade”, she added.
In addition, ClearDox is exploring the concept of consensus agents – multiple AI agents that evaluate and cross-check each other to determine whether a result is reliable – further increasing confidence in the system’s outputs.
By the end of the demonstration, I had the impression that I had just seen a glimpse of the future of back-office applications.
Read the original article on CTRMCenter. →