AI Spots Money Mules Before the Money Moves

194,000 money mule accounts were offboarded in the UK between January 2022 and September 2023. That number is big, but the more telling detail is what came next: only 37% of those mules were reported to the National Fraud Database.

That gap is the story. Banks are detecting mule activity, but the industry still struggles to share it fast, act on it early, and stop it before it turns into irreversible loss—especially now that real-time payments move funds in seconds.

This post is part of our AI in Cybersecurity series, and money mules are one of the clearest case studies of why traditional, rules-heavy fraud controls hit a ceiling. The winning approach in 2026 is offense: AI-driven behavioral analytics that spots mule risk across the full account lifecycle—application, onboarding, first transactions, device changes, and every “odd” transfer that looks normal in isolation.

Why money mules force banks to play offense

Money mules are the connective tissue of modern financial crime. They receive illicit funds and pass them along—sometimes knowingly, sometimes while being manipulated. The effect is the same: mule networks turn single scams into scalable money laundering pipelines.

Defense-only thinking breaks down for three reasons:

1. Speed beats investigations. With instant payments, you don’t get a comfortable window to review a suspicious transfer. If you’re waiting for certainty, you’re already late.
2. Proof standards are high. Many institutions hesitate to label someone a mule without strong evidence. That’s understandable legally, but operationally it means mule intelligence is under-shared and under-used.
3. Mule behavior is adaptive. Criminal handlers constantly tweak recruitment channels, transaction patterns, and device behaviors to slip past static rules.

Here’s the stance I take: if you treat mule detection as a “transaction monitoring” problem only, you’ll keep losing. It’s an identity + behavior + network problem, and AI is built for exactly that kind of messy signal.

The five money mule personas—and the signals AI can catch

A practical way to operationalize mule detection is to stop searching for a single “mule pattern” and instead map detection to personas. The Dark Reading piece outlines five that show up repeatedly.

The Deceiver (intentional fraudster)

Answer first: Deceivers are best stopped at account opening and early lifecycle moments, where AI can connect identity risk and behavioral oddities before the first big transfer.

Deceivers may use stolen or synthetic identities and aim to look “just legitimate enough” to pass onboarding. Traditional controls often focus on document validity and KYC checklists. Useful, but not sufficient.

AI improves early detection by scoring risk across:

• Onboarding behavior: copy/paste patterns, rapid form completion, inconsistent navigation, repeated edits on identity fields
• Device and network signals: device reputation, emulator/remote access indicators, IP/ASN anomalies, impossible travel
• Identity graph clues: reused phone/email patterns, address anomalies, shared device fingerprints across “different” customers

The goal isn’t to “auto-deny everyone.” It’s to route high-risk openers into stepped-up verification and slow them down long enough to prevent rapid monetization.

The Peddler (sells account access)

Answer first: Peddlers are caught by detecting account control changes—new devices, new biometrics, new usage rhythms—more than by transaction rules.

Peddlers often start as genuine customers and later sell access. That makes them dangerous: their history looks clean until it suddenly doesn’t.

What works in practice is a “control integrity” model that flags:

• New device + new location + new payee setup within a short window
• Sudden shift from normal bill pay to high-velocity P2P transfers
• Login patterns consistent with scripted access or remote tooling

If you’ve built your fraud stack around static customer profiles, this persona will repeatedly slip through. Behavioral AI shines because it can learn “how this person usually behaves” and call out the day that stops being true.

The Accomplice (willing middleman)

Answer first: Accomplices are detected by fund velocity and destination risk—how fast money moves through the account and where it goes.

Accomplices mix normal life activity with criminal transfers, which is why simple threshold rules (“flag transfers over $X”) fail. They keep transfers below limits, spread them across payees, and use P2P rails.

AI can raise the signal-to-noise ratio by combining:

• Velocity features: time between inbound credit and outbound transfer, number of hops in a short period
• Payee network analysis: shared payees across unrelated customers, clustering around known mule hubs
• Behavioral drift: subtle shifts in transfer frequency, time-of-day patterns, and channel usage

One snippet-worthy truth: mule networks leave a network-shaped shadow, even when each single account looks “fine.”

The Misled (unknowing participant)

Answer first: Misled mules are identified by context mismatch—transactions that technically “make sense,” but don’t match the customer’s life pattern or story.

These are the hardest cases and the most ethically sensitive. Think fake jobs (“payment processing agent”), marketplace scams, or romance fraud fallout. You want to stop the laundering and protect the customer.

AI helps by correlating:

• Unusual inbound sources (new counterparties, atypical payment references)
• First-time behaviors (first wire, first crypto exchange transfer, first international beneficiary)
• Abrupt changes following risky digital events (phishing click indicators, credential stuffing attempts, suspicious session patterns)

Action matters here. A good program doesn’t just block—it intervenes:

• real-time warnings (“This payment looks linked to a common scam pattern”)
• step-up authentication
• a fast path to a fraud specialist who knows how scam victims respond

The Victim (exploited account)

Answer first: Victim-mule activity often overlaps with account takeover, so detection should prioritize session-level anomalies and device compromise indicators.

These customers didn’t choose to mule. Fraudsters log in, add payees, and push funds through the victim’s account.

Your best defenses are:

• Behavioral biometrics and interaction analytics (typing cadence, mouse/touch patterns)
• Device binding and continuous authentication
• Transaction “intent checks” when payees or transfer limits change

If your authentication is “strong” only at login, you’re leaving a huge gap. The mule moment often happens after the login succeeds.

What “proactive mule detection” looks like in an AI-driven program

Answer first: Proactive mule detection means scoring risk continuously from onboarding through daily activity, then triggering friction before funds exit the institution.

Banks talk about being proactive, but I’ve found the difference comes down to three operational choices.

1) Monitor the full account lifecycle (not just payments)

Treat onboarding, logins, device changes, payee creation, and beneficiary edits as first-class fraud events.

A practical control stack:

• Pre-account: identity + device risk scoring
• Early-life (first 7–30 days): heightened behavioral baselining and velocity limits
• Steady state: anomaly detection against personal baselines and peer groups
• High-risk moments: payee add, new device, password reset, SIM change indicators, remote access tools

2) Combine three AI lenses: behavior, anomalies, and networks

Many programs pick one.

• Behavioral analytics catches “this isn’t the real customer” and “this account changed hands.”
• Anomaly detection catches “this flow is weird” even when amounts are small.
• Graph/network models catch “this customer is connected to a mule cluster.”

Together, they reduce false positives while improving early interception—exactly what real-time rails demand.

3) Turn detections into playbooks, not just alerts

If your SOC or fraud team gets 500 mule alerts a day, you don’t have detection—you have noise.

The playbook should specify:

1. What to do in the next 60 seconds (delay transfer, step-up auth, confirm intent)
2. What to do in the next 60 minutes (case creation, counterparty review, network check)
3. What to do in the next 7 days (offboarding decision, customer education, recovery attempts, reporting workflow)

This is where AI helps again: prioritization, summarization, and automated case narratives (with human review) can cut handling time dramatically.

Data sharing: the missing multiplier

Answer first: Mule networks don’t respect bank boundaries, so cross-industry intelligence sharing is necessary to stop “whack-a-mole” offboarding.

The source article highlights a painful reality: institutions may offboard mules, yet reporting and data sharing still captures only a fraction. The result is predictable—the network routes around the bank that caught them.

Even without naming specific systems or sending external links, the principle stands: banks need standardized, privacy-aware ways to share:

• mule typologies and behaviors
• risky payee clusters and routing patterns
• device and session risk indicators (in aggregated form)

AI models get better with broader, cleaner signal. But they also introduce governance questions—bias, explainability, and escalation paths. If you’re building an AI fraud program for lead-worthy outcomes, prioritize these guardrails early:

• Model explainability for investigators (top features, similar historical cases)
• Human-in-the-loop for adverse actions (holds, closures, reporting)
• Testing for disparate impact across customer segments

My opinion: “AI-first” without governance becomes a liability. “AI-first with guardrails” becomes a competitive advantage.

Practical checklist: what to implement in the next 90 days

Answer first: You can reduce mule exposure quickly by hardening early-life monitoring, device/control integrity checks, and velocity-based interventions.

Here’s a 90-day plan that doesn’t require a multi-year platform rebuild:

1. Create a mule persona matrix
   • Map each persona to signals, controls, and owner teams (fraud, IAM, SOC, CX)
2. Add early-life risk tiering
   • First 30 days: tighter limits, more step-up, faster review SLAs
3. Instrument “control integrity” events
   • New device, new payee, password reset, email/phone change, unusual session tooling
4. Deploy velocity + time-to-dispersal features
   • Measure time between inbound and outbound; trigger holds or confirmations
5. Stand up a mule network view
   • Even basic graph analytics (shared payees, shared devices) finds clusters
6. Write customer-safe interventions
   • Warnings and confirmations designed for scam victims, not just criminals

If you only do one thing: treat payee creation and beneficiary changes as high-risk events and wrap them in behavioral checks. It stops a surprising amount of mule flow.

Where this is heading in 2026

Money mule recruitment is getting more efficient, not less—especially through social platforms and “easy cash” narratives that spike around holiday and post-holiday financial pressure. That seasonal reality matters in December: more people are stressed, more are receptive to bad offers, and more accounts see unusual activity.

Banks that win won’t be the ones with the strictest rules. They’ll be the ones that can say, confidently and quickly: “This account is behaving like a mule, and we can prove why.” That’s the promise of AI in cybersecurity when it’s applied to fraud prevention with care.

If you’re building your 2026 roadmap, ask a direct question: are you still trying to catch mules after the money moves—or are you set up to stop them in the moment it starts?