Collusion & Money Mule Networks: The Backbone of Modern Fraud

Behind nearly every high-dollar fraud scheme lies a network. These aren’t just one-off criminals—they’re organized groups working in tandem, with each actor playing a specialized role. From international cybercriminals to unwitting college students lured by the promise of quick money, these collusion-based networks are driving a significant portion of today’s financial crime.

For financial institutions, identifying these networks—and the money mules they depend on—is no longer optional. It’s central to an effective fraud prevention strategy.

Understanding the Role of Collusion in Financial Crime

Collusion-based fraud involves two or more individuals working together to commit or facilitate fraud, often across multiple institutions or accounts. These schemes can be as small as a customer conspiring with a bank insider, or as vast as a transnational ring operating mule accounts across hundreds of banks.

In the case of check fraud, for example, one actor may steal checks, another alters or counterfeits them and a third deposits them through mule accounts. In business email compromise (BEC), the fraudster overseas might never touch the funds—they rely entirely on U.S.-based mules to receive and reroute stolen payments. Romance scams, tech support scams, unemployment fraud—nearly all of them share one thing: proceeds must be moved, and that’s where money mules come in.

Who Are the Mules?

Money mules can be knowingly complicit or completely unaware they’re facilitating a crime. In some cases, individuals respond to fake job ads or are manipulated through online relationships. In others, they’re recruited directly with promises of a quick payday. The rise of social media and gig economy job boards has made mule recruitment easier and more scalable than ever.

Certain demographics are more frequently targeted. Younger individuals, especially those in college or early career stages, are often recruited as mules. At the same time, older adults may have their accounts hijacked to serve as pass-through channels. In either case, these networks create a web of activity that obscures the original source of stolen funds.

The Detection Dilemma

The primary challenge with collusion-based fraud is that no single transaction always looks suspicious. It’s only when the full picture is assembled—across accounts, geographies and timelines—that the fraud becomes visible.

Institutions face several obstacles:

• Mule accounts often appear legitimate during onboarding, with clean histories and valid credentials.
• Transaction layering is common, where funds move across multiple accounts in small increments.
• Geographic dispersion allows fraud rings to operate below detection thresholds by spreading activity across different branches and institutions.

Sometimes the only clear signal is an emerging pattern: a cluster of new accounts receiving ACH deposits from unrelated sources, followed by rapid cash withdrawals, or multiple customers funneling money to a single recipient with no clear connection.

Traditional fraud systems, focused on singular events or individual profiles, often miss these patterns. That’s why network analysis and cross-channel behavioral analytics are essential.

Law Enforcement and Regulatory Crackdown

Federal agencies have recognized the critical role money mules play in enabling fraud. Each year, the Department of Justice leads a “Money Mule Initiative,” targeting thousands of individuals identified through financial intelligence and SAR data. In the 2023–2024 cycle alone, law enforcement took action against over 3,000 money mules involved in scams ranging from lottery fraud to pandemic relief abuse.

Regulatory bodies like FinCEN have made it clear: institutions should monitor and report suspected mule activity. SARs should include contextual details—especially indications that multiple actors are involved or that funds are moving through relay accounts. FinCEN also continues to issue typology-specific alerts (BEC, elder fraud, synthetic identity) that highlight the role of money mule networks in laundering illicit funds.

Importantly, institutions have been encouraged to share information under Section 314(b) of the USA PATRIOT Act, enabling coordination that spans multiple financial providers.

Proactive Approaches to Disrupting Networks

To combat collusion, institutions must move from reactive investigation to proactive network interdiction. That means identifying not just the suspicious account, but the relationships, behaviors and devices connecting it to others.

Modern fraud detection solutions are rising to this challenge by incorporating:

• Entity resolution and link analysis, which detect relationships between accounts, even when identity elements vary
• Geolocation and device fingerprinting, to track when the same device accesses multiple “unrelated” accounts
• Behavioral analytics, which can detect anomalies in transaction volume, speed, and beneficiary patterns

Some institutions are even using voice biometrics and call center analytics to flag repeat callers impersonating different individuals—a tactic commonly used by mules managing multiple compromised accounts.

Equally important is internal education. Training frontline staff to recognize red flags, such as a new customer making large withdrawals, or someone appearing to coach another individual through a transaction, can provide the earliest opportunity to intervene.

How NICE Actimize Helps Financial Institutions See the Bigger Picture

NICE Actimize provides financial institutions with advanced analytics and network detection capabilities purpose-built to identify collusion and disrupt money mule activity. Our solutions help institutions connect the dots across accounts, detect suspicious patterns at scale and coordinate timely interventions—while also streamlining SAR filing and compliance workflows.

With real-time alerts, cross-channel visibility and configurable red flag indicators, our technology equips fraud teams to act quickly and confidently—before stolen funds disappear through the next layer.