To efficiently track down and investigate money mules, you need context. Investigating a mule account is largely about identifying the networks around that account and how they operate. Who is transferring money where? Which entities share information? Who has relationships with whom?
Tools based on graph analytics display even the most complex networks and relationships in a couple of clicks. This puts investigators a step ahead of money mules:
Quickly see and understand the context around any suspicious entity or transaction to get to the bottom of a mule account or network in no time.
Graph analytics lets you spot and connect multiple weak signals indicative of mule accounts that traditional rules-based systems might miss.
Faster, more efficient investigations
Fewer false negatives
Get the big picture
Monitor money flows from multiple angles to grasp the full extent of a money mule scheme and ensure that no bad actor goes undetected.
Using graph analytics to track down money mules
Mules spin a complex web
Fraudsters are specialists in avoiding raising red flags. Detection systems also fall short all too often. These are some of the biggest challenges financial institutions face in combating the money mule problem:
Criminals further complicate detection by exploiting cross-border operations and advanced anonymity techniques, creating complex financial paths that standard anti-fraud systems have difficulties in tracing.
Mule accounts look “normal”
Trusted accounts with clean transaction histories are less likely to trigger suspicion, allowing criminals to move funds through legitimate channels without immediate detection. Instead of large, high-risk transfers, they use small, frequent transactions designed to bypass traditional fraud detection systems.
Conventional fraud detection falls short
Most systems are designed to flag high-value, isolated transactions. Transactions involving money mules are practically designed to bypass such systems.
This gap in detection shows the urgent need for more sophisticated solutions to identify and uncover the full extent of these complex criminal networks.
Identifying money mules: An uphill battle
As financial institutions aim to combat the growing threat of money mules, they face the complex task of identifying and investigating mule accounts amidst a sea of legitimate accounts, customers, and transactions.
It can feel like traditional anti-fraud tools are stuck in the past, while criminals are rewriting the rules. But there is a better way.
$3 billion
in fraudulent financial transfers linked to money mules
How to reduce investigation time from hours to minutes
Mule Accounts
See graph analytics in action
Want to see how financial crime investigators unmask mule accounts? Go through an investigator’s journey from initial alert to final case resolution, showing how graph analytics can uncover an entire money mule operation in minutes rather than days.
Because you can't catch what you can't see
Financial crime resources
Fraudsters and money launderers know how to slip through the cracks of traditional detection systems, exploiting gaps across departments, customer journeys, and siloed systems to evade notice.
To stop them, you have to see them. And that starts with piecing together the big picture of your data.
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