The LaundroGraph: Bad Actors Be On Your Toes

January 20, 2023

Now here is a valuable use of machine learning technology. India’s DailyHunt reveals, “This Deep Learning Technology Is a Money-Launderer’s Worst Nightmare.” The software, designed to help disrupt criminal money laundering operations, is the product of financial data-science firm Feedzai of Portugal. We learn:

“The Feedzai team developed LaundroGraph, a self-supervised model that might reduce the time-consuming process of assessing vast volumes of financial interactions for suspicious transactions or monetary exchanges, in a paper presented at the 3rd ACM International Conference on AI in Finance. Their approach is based on a graph neural network, which is an artificial neural network or ANN built to process vast volumes of data in the form of a graph.”

The AML (anti-money laundering) software simplifies the job of human analysts, who otherwise must manually peruse entire transaction histories in search of unusual activity. The article quotes researcher Mario Cardoso:

“Cardoso explained, ‘LaundroGraph generates dense, context-aware representations of behavior that are decoupled from any specific labels.’ ‘It accomplishes this by utilizing both structural and features information from a graph via a link prediction task between customers and transactions. We define our graph as a customer-transaction bipartite graph generated from raw financial movement data.’ Feedzai researchers put their algorithm through a series of tests to see how well it predicted suspicious transfers in a dataset of real-world transactions. They discovered that it had much greater predictive power than other baseline measures developed to aid anti-money laundering operations. ‘Because it does not require labels, LaundroGraph is appropriate for a wide range of real-world financial applications that might benefit from graph-structured data,’ Cardoso explained.”

For those who are unfamiliar but curious (like me), navigate to this explanation of bipartite graphs. The future applications Cardoso envisions include detecting other financial crimes like fraud. Since the researchers intend to continue developing their tools, financial crimes may soon become much trickier to pull off.

Cynthia Murrell, January 20, 2022


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