Trade-Based Money Laundering Detection FAQs

Question 1

How does it detect complex TBML schemes?

Accepted Answer

The tool integrates with 14 data sources and employs five peer-reviewed forensic algorithms, including a Modified Benford-Grubbs hybrid for invoice anomalies, Tarjan's SCC for carousel fraud, and logistic regression for phantom entities, along with FX arbitrage detection.

Question 2

What are the main benefits of using this automated TBML detection system?

Accepted Answer

It dramatically reduces investigation time from days to seconds and significantly cuts costs. The system offers advanced detection capabilities for patterns like over-invoicing, carousel fraud, and phantom entities that are often missed by manual reviews, providing a composite TBML Risk Score and full audit trails.

Question 3

How does this tool integrate with existing AI workflows?

Accepted Answer

It operates as an MCP (Model Context Protocol) server, plugging directly into AI agents like Claude, Cursor, or Windsurf. Analysts can trigger investigations with natural-language prompts, receiving structured reports without needing to write any code.

Question 4

What is the Trade-Based Money Laundering Detection tool?

Accepted Answer

This is an AI-powered MCP server designed to automate the detection of trade-based money laundering (TBML). It helps compliance teams, AML investigators, and financial intelligence units rapidly identify illicit financial flows through global trade channels.

Question 5

Can it assist with regulatory reporting and compliance?

Accepted Answer

Yes, it automatically generates comprehensive FinCEN SAR Form 111 evidence packages. These packages include specific regulatory references, severity ratings, and narrative templates, streamlining your reporting and ensuring compliance with 31 CFR 1020.320.

Trade-Based Money Laundering Detection

Trade-Based Money Laundering Detection

Key Features

Use Cases

Key Features

Use Cases