The graphic illustrates an example of movement of funds through a mule network. Prior to receiving fraudulent funds, mule account holders make small arbitrary credits and debits to establish a payment history.
In this context, the £10,000 payment from a fraud victim looks atypical and can be used as a reliable fraud indicator.
Differentiating between legitimate and suspicious activities from a vast number of daily interactions and transactions can be complex. While it’s easier to spot patterns within an organisation’s own customer interactions, difficulty arises when tracking patterns that involve external entities, especially when funds transfer outside the bank’s direct oversight.
The foundation for effectively identifying and interrupting more mule accounts rests on the ability to understand the parties involved more holistically, from digital and physical identities and associated attributes, events and behaviours, to the parties’ accounts, interactions and transactions.
Transform human interactions into actionable intelligence.
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