AI Fraud Detection: Spot Money Mules Before They Strike

194,000 money mule accounts were offboarded in the UK between January 2022 and September 2023. And even then, only 37% were reported into the National Fraud Database in the most recent year of that review window.

That’s the uncomfortable truth about money mules: if your strategy depends on “confirmed cases,” you’re already late. Mules aren’t a niche problem at the edge of banking—they’re the plumbing that makes large-scale fraud and money laundering work.

For this post in our AI in Cybersecurity series, I’m taking a firm stance: traditional fraud controls are too defensive for real-time payments. Banks need to go on offense—using AI-driven behavioral analytics and transaction anomaly detection to identify mule activity early, disrupt networks faster, and reduce customer harm.

Why money mules force a shift from defense to offense

Money mule risk is a lifecycle problem, not a single-event problem. The account opening may be the beginning, the first inbound transfer may be the trigger, and the outbound “cash-out” may be the damage—but most legacy controls focus on the last step.

Real-time payments amplify the downside. Once funds move, they disperse across multiple institutions and channels, and recovery becomes expensive, slow, and usually unsuccessful. That reality changes the job:

• Defense-first fraud programs prioritize investigations after alerts fire and losses occur.
• Offense-first fraud programs prioritize early signals and disruption before the mule becomes operational.

This matters because mule behavior is rarely “one weird transaction.” It’s usually a pattern: device changes, new payees, velocity spikes, odd login behavior, unusual amounts, and shifting destinations. Those weak signals are exactly where AI fraud detection performs best.

The hidden metric that should worry fraud leaders

A mule account that gets closed is visible. A mule account that’s never reported into shared databases isn’t.

High proof standards, internal thresholds, and operational constraints mean industry databases capture only a fraction of mule activity. So if your detection depends heavily on “known bad,” you’re building a rear-view mirror, not radar.

The five mule personas—and what AI can see that rules miss

Different mule personas produce different signals. Treating mules as one monolithic category is how teams end up with blunt rules, high false positives, and a backlog of alerts nobody can clear.

Below are the five personas highlighted in the source material—translated into practical detection ideas for AI-driven cybersecurity and fraud teams.

1) The Deceiver (intentional fraudster)

Direct answer: Deceivers can be caught early by combining identity risk with behavioral signals during onboarding.

Deceivers open accounts specifically to commit fraud, often using identity fraud or synthetic identities. They may look “clean” on day one, but their early journeys tend to show telltale friction patterns: scripted form fills, unusual device fingerprints, suspicious document capture behavior, or mismatches between claimed profile and observed digital behavior.

What works well in practice:

• AI-based onboarding risk scoring that combines KYC results with behavioral biometrics
• Device intelligence to detect emulator use, device farms, or impossible travel
• First-72-hours monitoring (Deceivers often activate quickly)

My opinion: onboarding controls that stop at document verification are incomplete. The fraud economy has learned how to pass checks; it hasn’t learned how to mimic human behavior consistently.

2) The Peddler (sells account access)

Direct answer: Peddlers stand out when an account’s “digital identity” changes faster than a legitimate customer’s would.

Peddlers often start with legitimate-looking accounts, then sell access. That means the account’s transaction history may be normal—until it isn’t. The key is detecting ownership-like changes even when legal ownership hasn’t changed.

Signals that AI can correlate better than rules:

• New devices + new IP ranges + new session timing patterns
• Sudden changes in typing/mouse/mobile touch behavior
• New payees plus unusual payment initiation flows
• A jump in remote access tools or risky browser configurations

Why this is a cybersecurity problem too: account resale and account takeover sit on the same spectrum. Your fraud team and SOC should treat “account integrity” as a shared mission.

3) The Accomplice (willing middleman)

Direct answer: Accomplices are detected by spotting abnormal money movement patterns while allowing normal daily spending.

Accomplices knowingly receive and transfer illicit funds for profit, but they often keep day-to-day activity intact (groceries, subscriptions, bills) to blend in. The giveaway is fund velocity and destination behavior that doesn’t match their historical baseline.

AI performs well when you model:

• Inflow/outflow timing (how fast funds exit after entry)
• Amount “shape” (rounded numbers, repetitive increments, or threshold-hugging)
• Destination novelty (new payees, new banks, new P2P handles)
• Network signals (shared counterparties across multiple suspected mules)

If you’re relying on static thresholds like “>$X per day,” you’ll miss the accomplice who runs $200, $300, $450 repeatedly across P2P rails.

4) The Misled (unknowing participant)

Direct answer: Misled mules require context-aware analytics that link payments to behavioral intent, not just transaction fields.

Misled users think they’re doing something legitimate: a “job” that pays them to route funds, an online sale, a romance scam narrative. They’re hard because they may genuinely authenticate and behave normally.

Where AI helps is combining transaction anomalies with journey context:

• Payment sources inconsistent with the user’s typical counterparties
• Sudden new “income” with immediate redistribution
• Chatty customer support contacts plus urgent payment behavior
• Device behavior indicating guided steps (copy/paste patterns, repeated screen switching)

Operationally, your response should be different here. The goal isn’t only to block—it’s also to interrupt safely, warn the customer, and prevent repeat victimization.

5) The Victim (exploited by fraudsters)

Direct answer: Victim mule activity is best detected through account takeover indicators paired with high-risk transfer patterns.

Victims are used as conduits after fraudsters gain access. This overlaps heavily with cybersecurity controls: credential stuffing, session hijacking, social engineering, and SIM swap scenarios.

Strong detection combines:

• Login anomaly detection (new device, unusual geo, abnormal session duration)
• Step-up authentication triggers (but only when risk is high)
• Transfer anomaly detection (new payees, unusual velocity, first-time high-value)

The best programs treat this as identity security + fraud—not two separate queues.

What “AI on offense” looks like in a bank (and why it’s not just more alerts)

Offense doesn’t mean blocking more transactions; it means intervening earlier with higher confidence. In mature programs, AI changes three things: timing, precision, and coordination.

Earlier: focus on pre-loss indicators

The most valuable signals often appear before the first “obvious” fraud event:

• Risky onboarding journeys
• “Warm-up” behavior (adding payees, linking external accounts)
• Session patterns that resemble coaching or automation

AI models that score these sequences can trigger lightweight friction early (verification, cooling-off periods, education prompts) instead of heavy-handed blocks later.

More precise: behavioral analytics reduces false positives

Rules struggle because mule behavior is adaptive. Criminals rotate amounts, payees, and channels.

Behavioral analytics looks at how actions happen, not only what happened:

• How the user navigates to payments
• How quickly they create payees and initiate transfers
• Whether their biometric behavior shifts abruptly

That’s why many banks have used machine learning to identify mule accounts at scale (the referenced BioCatch research cites nearly 2 million mule accounts identified last year by banks using ML).

Better coordinated: fraud + cybersecurity + AML operate as one system

Money mules sit at the intersection of:

• Fraud (customer harm, scams, reimbursement)
• Cybersecurity (ATO, bot activity, session abuse)
• AML/financial crime (laundering typologies, reporting obligations)

AI can’t fix organizational silos on its own, but it can provide a shared risk language: a unified score, linked entities, and case clustering that shows teams they’re chasing the same actor from different angles.

A practical playbook: building mule detection across the account lifecycle

The best mule detection programs treat every account like a story with chapters. That means instrumenting risk from onboarding through dormancy through reactivation.

Here’s a concrete, bank-ready approach that I’ve seen work.

Step 1: Score risk at onboarding (and keep scoring)

Don’t treat KYC as a pass/fail gate. Treat it as the first data point in a living model.

• Combine identity verification results with device reputation and behavioral signals
• Track “application velocity” (how quickly accounts are opened across similar devices)
• Flag synthetic identity indicators for enhanced monitoring, not just rejection

Step 2: Monitor activation windows (0–7 days)

Many mule accounts “turn on” quickly. Build dedicated monitoring for:

• First inbound transfers from new sources
• Rapid payee creation
• Immediate high-velocity outbound activity

This is where real-time anomaly detection earns its keep.

Step 3: Model money movement behavior (velocity + destinations)

Mule accounts have distinct movement patterns:

• Short dwell time for funds
• Repeated transfers to new or loosely related destinations
• Channel hopping (bank transfer → P2P → cash-out)

Use graph analytics to connect accounts by shared counterparties, devices, and beneficiary clusters.

Step 4: Automate the right interventions

Automation should be surgical. A few examples:

• Step-up auth when device behavior shifts abruptly
• Delayed settlement or cooling-off on first-time high-risk payees
• Just-in-time scam warnings when patterns match known recruitment flows
• Temporary transfer limits tied to risk score (not one-size-fits-all)

The reality? A well-timed 30-second interruption often prevents a loss without blowing up the customer relationship.

Step 5: Share intelligence across institutions (without waiting for certainty)

Mule networks don’t respect bank boundaries. Cross-industry sharing is critical, but the practical challenge is proof thresholds.

A smart compromise is sharing risk signals and typologies earlier—even if you’re not ready to label an account “confirmed mule.” That can include:

• Newly observed beneficiary clusters
• Device and infrastructure indicators
• Emerging scam narratives tied to mule recruitment

Questions teams ask before they invest in AI for mule detection

“Will AI replace our fraud analysts?”

No. It changes what they spend time on. The win is fewer low-quality alerts and more clustered, explainable cases that humans can act on.

“Is this fraud, AML, or cybersecurity?”

It’s all three. Money mules are the bridge. If your internal ownership model can’t handle that, your detection will stay fragmented.

“What’s the fastest way to show ROI?”

Start with one high-impact slice:

• real-time payments + new payees + high velocity
• onboarding + first-week behavioral monitoring
• ATO signals + outbound transfer anomalies

Measure reduced losses, reduced investigation time, and fewer repeat victim cases.

Where to take this next

Money mules thrive in the gap between what banks can prove and what banks can predict. That gap is widening as real-time payment adoption grows.

If you’re building an AI fraud detection program as part of a broader AI in cybersecurity strategy, aim for offense: continuous behavioral analytics, lifecycle monitoring, and coordinated response across fraud, AML, and the SOC.

If you want a second set of eyes, I’m happy to help you map your mule detection coverage to these five personas and identify the fastest path to measurable impact. What would change in your fraud outcomes if you could stop mule behavior in the first week—rather than after the cash-out?