This article in FinTech Futures written by David Rich of Vocalink, a Mastercard company, provides data that highlights the scale of financial crimes, but more importantly continues with a description of a machine learning model that evaluates data from multiple institutions to detect and track illicit accounts and funds:
“Previously, while banks have been able to detect and investigate suspicious payment activity within their own four walls, they have been unable to see a detailed picture of how stolen or illicit funds are dispersed across a network. Significant progress has now been made with the launch of a ground-breaking solution to overcome this hurdle.
In December 2018, Pay.UK, the UK’s retail payments authority and paytech firm Vocalink launched the Mule Insights Tactical Solution (MITS), which is underpinned by Vocalink’s Anti-Money Laundering (AML) Insights solution.
By bringing together payments data from multiple banks and overlaying it with proprietary analytics and algorithms, MITS accurately pinpoints individual accounts (known as mule accounts) involved in suspected illegal activities.
It also enables suspicious payments to be tracked as they move between bank and building society accounts, regardless of whether the payment amount is split between multiple accounts, or if those accounts sit within the same or different financial institutions. MITS creates a visual map of where and when money has moved, providing data driven insights and new intelligence that enable banks’ financial crime teams to take action.
The launch followed a successful pilot with a number of major banks. As well as uncovering multiple money laundering rings, the pilot also discovered hundreds of mule accounts previously completely unknown to the participating banks.
Over 90% of UK current accounts are now covered by the technology and the AML Insights technology can be applied to payment systems around the world.”