Lynx, a provider of artificial intelligence (AI) software that detects and prevents fraud and financial crimes, has introduced a real-time mule detection solution for banks and financial institutions.
The solution helps banks swiftly identify and prevent money mule activities, addressing global regulatory demands and significant laundering operations.
Cifas, the not-for-profit UK fraud prevention service, estimates that 37,000 British bank accounts exhibited behaviour associated with muling in 2023. These accounts facilitate approximately £10billion of laundering each year in the UK, with around 23 per cent conducted by individuals under 21 and 65 per cent by those under 30.
In the US, money mule accounts represent up to 0.3 per cent of accounts held by financial institutions, and an estimated $3billion in fraudulent financial transfers. While in 2022 law enforcement organisations from 25 countries arrested 2,469 money mules in a worldwide crackdown against money laundering.
The use of machine learning and automated tactics by organised crime gangs has made detecting mule accounts more challenging, as they can effortlessly create hundreds of accounts. Furthermore, the widespread adoption of instant payments shortens the window to identify and block mule transactions.
Digital onboarding, designed to streamline the identification and verification (ID&V) process, has unintentionally enabled the proliferation of mule accounts. This problem is worsened by a tenfold increase in deep fakes globally from 2022 to 2023. To effectively prevent the real-time creation of mule accounts, the industry requires a real-time money mule prevention solution.
Lynx detection tool
The Lynx Mule Account Detection enables swift identification of mules to prevent fraud and facilitate real-time reporting of suspicious activity, without requiring a wholesale integration of fraud and AML teams.
The solution uses supervised machine learning to detect illicit funds and mule accounts in real time, offering actionable insights for immediate response. It enables analysts to swiftly prevent fraud by reducing the time spent reviewing alerts.
By integrating both incoming and outgoing transactions, the model flags and blocks mule accounts. It identifies irregular fund sources, such as those from authorised push payment fraud (APPF), and flags mule accounts in real time. Financial institutions benefit from reduced financial losses by preventing fraudulent payments.
This launch coincides with increasing global regulatory demands requiring financial institutions to accept financial responsibility for APPF transactions. For instance, new UK regulations effective from 7 October mandate that financial institutions refund victims of APPF fraud, underscoring the critical need for advanced real-time mule detection solutions.
Disrupting the flow
Dan Dica, CEO of Lynx, said: “Stopping money mules doesn’t just matter for financial institutions; it matters for everyone. Money mules are a critical link in the chain of financial crime, as they facilitate the movement of illicit funds across the globe. By disrupting this flow, we not only protect countless victims but also cripple the operational capacity of criminal enterprises.”
The product’s proprietary Daily Adaptive Model used continually updated models based on the latest financial behaviour enabling the accurate identification of genuine users and criminals. Ongoing updates maintain highest accuracy while dramatically reducing false positives and their associated cost.
“Even with an existing fraud solution in place, leveraging Lynx’s money mule model scoring enhances the detection of money mules, addressing this specific challenge effectively without the need for complex integrations,” Dica also added. “What this solution makes possible is a world where criminal rings can’t operate, because their financial pipelines are blocked at every turn.”
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