AI Agentic Workflow for Trade Finance and Letter of Credit Surveillance

Manual review of shipping documents, invoices, and bills of lading for discrepancies can take analysts days per alert. A custom workflow automates document ingestion via OCR/LLM, cross-references data against trade platforms (e.g., CGI Trade360, SAP), and flags mismatches in price, quantity, or goods description in minutes. This compresses the initial triage phase from hours to seconds, allowing investigators to focus on high-risk, complex cases.

Rule-based systems generate excessive alerts on legitimate trade variations, creating analyst fatigue. A custom workflow uses multi-agent reasoning to apply contextual logic: an agent checks dual-use goods lists, another validates vessel ownership against sanctions databases, and a third assesses the financial history of the trading parties. This layered, contextual analysis suppresses noise, ensuring only substantively suspicious activity escalates for review.

Building a case dossier requires pulling data from fragmented systems: core banking, trade finance platforms, external sanctions feeds, and shipping databases. An orchestrated agentic workflow automates this synthesis. A primary agent acts as a conductor, triggering sub-agents to retrieve and normalize data from each source, assembling a preliminary investigation package before an analyst logs in. This eliminates up to 3-5 hours of manual prep work per case.

Regulators demand evidence of a risk-based, continuous monitoring program. A custom workflow embeds explainability and governance by design. Every agent decision, data source queried, and red flag scored is logged to an immutable audit trail. Automated reporting generates compliance-ready documentation, demonstrating proactive surveillance and a controlled, repeatable process that withstands examination.

Implementation risk is managed through a phased, use-case-first approach. A pilot focuses on a single high-risk trade corridor or product (e.g., Letters of Credit for commodities). Using frameworks like LangGraph for orchestration, the workflow integrates with existing APIs from trade platforms and screening vendors. This targeted build delivers measurable ROI within two months, proving the architecture before scaling to full portfolio coverage.

Move from batch-based, after-the-fact reviews to a continuous control layer. The workflow monitors transactions in near real-time, applying dynamic risk scoring that updates with new shipping data or sanctions list changes. This shifts the compliance function from a cost center reacting to alerts to a strategic unit that provides early warning on emerging TBML threats, protecting the bank's reputation and capital.

This agent connects to trade platforms (e.g., CGI Trade360, SAP GTS) and banking channels to ingest structured and unstructured documents—commercial invoices, bills of lading, letters of credit, and certificates of origin. It uses a hybrid of OCR and document-specific LLMs to extract key fields (parties, goods, values, quantities, ports) with high accuracy, normalizing data for downstream analysis and flagging missing or illegible documents for human retrieval.

The core detection agent applies rule-based logic and ML models to identify TBML indicators. It cross-references extracted data to flag mismatches in unit prices against global benchmarks, inconsistencies between invoice values and shipping weights, deviations from standard trade routes, and dual-use goods descriptions. This agent generates a risk score and preliminary alert, attaching the evidence bundle for review.

This agent maintains a dynamic graph of trade participants (buyers, sellers, shippers, notify parties). It resolves entities across documents and transactions, screens them against internal watchlists and external sanctions/PEP databases, and maps ownership structures. It identifies circular trades, frequently changed counterparties, and complex networks designed to obscure beneficial ownership, enriching alerts with relationship context.

Upon alert generation, this orchestrator agent aggregates all relevant documents, entity profiles, historical transactions, and risk scores into a preliminary case dossier. It then routes the case based on configurable rules: high-risk/high-value alerts go to senior investigators via integrated case management systems (e.g., Actimize), while low-confidence alerts may be queued for junior review or automated for closure with an audit trail.

The workflow's backbone is a robust integration layer that handles bidirectional syncs with core banking systems, trade platforms, screening data vendors, and case management tools. It manages API authentication, rate limiting, error handling, and data transformation. This layer ensures the agents operate on fresh, operational data and that their outputs (alerts, cases) flow seamlessly into existing investigator workflows and audit logs.

A critical control component that logs every agent decision, data point, and user override for a defensible audit trail. It captures investigator feedback on alert validity, which is used to retrain ML models and calibrate rule thresholds automatically. This closed-loop system, governed by approval gates for model changes, ensures continuous improvement in detection accuracy and supports regulatory examinations.

Core Data Ingestion. Agents connect via API or secure file transfer to pull structured trade data—letters of credit, invoices, bills of lading—directly from the bank's trade finance platform. This eliminates manual data entry, ensures real-time monitoring, and provides the foundational transaction context for all downstream analysis. Implementation requires handling platform-specific schemas and ensuring idempotent data ingestion to prevent missed or duplicate alerts.

Unstructured Data Extraction. Invoices, packing lists, and certificates of origin are processed through a custom pipeline combining OCR (e.g., Azure Form Recognizer, Google Document AI) and LLM-based extraction agents. These agents normalize key fields (product descriptions, Harmonized System codes, unit prices, quantities) and flag poor-quality scans for human review. This transforms paper trails and PDFs into structured, analyzable data, automating a major manual bottleneck.

Contextual Risk Enrichment. Orchestration agents call external vendor APIs (e.g., Dow Jones, Refinitiv) and public databases to screen counterparties, vessel names, and goods against sanctions lists, PEP registers, and dual-use goods catalogs. Results are fused with transaction data to generate a composite risk score. The workflow manages API rate limits, cost optimization, and fallback logic to maintain surveillance continuity during vendor outages.

Alert Routing & Investigator Handoff. High-confidence alerts are automatically formatted and pushed as tickets into the bank's existing case management system. The integration includes relevant extracted data, risk scores, and suggested next actions. It also pulls back investigator decisions and notes to create a closed-loop feedback system for model retraining. This ensures the AI workflow augments—rather than replaces—the regulated investigative process.

Golden Record Context. Agents query the core banking system and customer data platform to retrieve complete client profiles, relationship history, and past alert records. This context is critical for distinguishing between legitimate complex trade and suspicious activity. The integration must handle high-volume, low-latency reads and respect data governance boundaries to avoid performance impacts on transactional systems.

Governance & Model Control. Every agent decision, data point ingested, and API call is logged to an immutable audit trail (e.g., using event sourcing). This layer integrates with monitoring tools like Datadog or Grafana to track system health, alert volumes, and model performance (precision/recall). It is non-negotiable for regulatory examinations and for demonstrating the control environment around the automated workflow.