Trade-based money laundering poses a significant threat to the integrity of global financial systems. Criminals exploit international trade mechanisms to disguise illicit proceeds, making detection exceedingly complex. Traditional Trade-based money laundering detection systems often fall short in identifying intricate patterns and relationships that indicate suspicious activities. However, innovative solutions like retrieval-augmented generation and semantic search technologies are revolutionizing the field by offering enhanced detection capabilities.

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About the author
Howard W. Herndon