By Liese Rushton, Fraud Strategy Consultant at Synectics Solutions
It’s no coincidence that both the FCA and NCA agree that mule detection must be a priority for banks. But the reality is, recognition of this fact hasn’t made response any more straightforward.
Money muling is growing and evolving rapidly. Launderers are playing the long game, with 75% hitting banks months after onboarding checks are complete. And with synthetic identities, atypical product targeting, and fragmented oversight, mule activity can easily blend into normal behaviour and your customer book.
To keep pace with financial crime, continuous on-book monitoring and alerting is absolutely necessary. Regulators agree with this approach too, with perpetual KYC and Data (Use and Access) Act encouraging and legitimating fast, data-driven and automated money laundering decisions.
The message to banks is clear: use automated AML controls that act faster and more accurately. But automation alone isn’t enough – especially without context.
Automation without context is fast but unfocused
Without context, always-on monitoring brings its own risks. Automation can quickly escalate into alert fatigue, floods of false positives and delays for genuine customers. Some of you reading this will no doubt be facing these exact pressures within your fraud and AML teams.
That’s because detecting more doesn’t mean detecting better.
To meet the goal – balancing prompt mule detection with operational integrity – automated mule detection must prioritise relevance over volume, and nuance over noise. And that starts with context. By context, we mean two things:
- Data, to give real-time behavioural insight into a customer’s financial profile, activity patterns, network of connections, and evolving risk footprint- across institutions, products, and sectors.
- And controls, in the form of alerting logic that reflects your specific risk appetite – not generic thresholds or blunt rules.
With context, risk signals and investigators are focused, regulators get explainability, and customers avoid unnecessary friction. But without it, continuous mule detection becomes a Pandora’s Box. Not the power tool it was meant to be.
Layering with AI: smarter monitoring or a bigger backlog?
AI is now a core consideration in continuous mule detection strategies and when applied well, it adds a valuable layer of control.
It can clear low-risk activity automatically, block obvious fraud, and triage what genuinely needs human attention – bringing efficiency to what would otherwise be an overwhelming stream of alerts.
But AI can only be as effective as the data it’s built on. That includes the ongoing mule signals it’s responding to, and broader intelligence that completes a picture of risk.
Without this high-context view, AI-powered automation threatens to trigger a wave of unnecessary alerts. And worse, making decisions at scale that you can’t fully explain or stand behind when challenged. Essentially amplifying the very challenges it was meant to solve.
If you’re considering using AI to help manage how alerts are handled, it’s vital that monitoring layer is fuelled by complete, high-context data that accurately reflects mule risk.
Two cautionary tales of untethered automation
It’s important to note that overwhelm and accuracy issues don’t just surface during major laundering events. Everyday banking activity can trigger a wave of false flags – if your mule monitoring data and controls aren’t aligned to real-world behaviour and your actual risk appetite.
Here are two examples of how untethered automation can spiral.
- A marketing campaign triggers first-party mule risk
A bank launches a new current account for 18–24-year-olds, offering fee-free overdrafts and lifestyle perks. Soon after launch, monitoring auto-flags high volumes of suspected mule activity:
- Rapid full-balance withdrawals to digital wallets
- Unusual usage within days of activation
Some cases are genuine. But many are simply low-income, high-velocity users behaving outside the bounds of legacy risk models.
With broader context – cross-sector patterns, age-based behavioural norms, and flexible controls – true signals rise above the noise, and AML teams focus on what counts.
- Cost of living pressures trigger risk signals in safe accounts
Low risk customers begin gig work as a second income stream as cost-of-living pressures bite. Gig earnings flow into their personal accounts, cash movements increase, and new connections grow. Outside the standard review window, continuous mule monitoring flags:
- Incoming payments from new platforms or third parties
- High-volume outbound transfers, often late at night or in rapid bursts
These changes can trigger unnecessary investigations. Unless cross-sector intelligence confirms activity elsewhere is benign, and payments are linked to trusted gig platforms.
Flexible controls enable temporary oversight – like spot checks or increased screening for a few months – without overwhelming teams.
Context: the safeguard against unnecessary escalation
Depth of context is essential for automated mule detection because it’s the only reliable way to prioritise actual laundering risk over statistical noise. It gives teams the confidence to act fast – whether through automated decisions or high-risk investigations – and gives regulators the clarity to understand why.
When embedded properly into workflows, context allows automation to focus teams, not flood them.
It supports human judgement and quality decision-making by aligning alerting logic to your actual risk appetite, not just statistical anomalies. And it delivers faster, more accurate outcomes without compromising due diligence or overloading investigators.
The goal isn’t more alerts, it’s better ones.
Organisations getting the most from continuous, automated mule detection are moving beyond generic red flags, toward signals that truly reflect risk. They’re focusing less on how often AML systems escalate alerts, and more on how confidently they can explain why.
Because ultimately, better decisions are the goal – and automation, including AI, is a tool to help you get there.
Mishandle that tool, and it becomes a Pandora’s Box – scaling confusion, eroding control, and sacrificing trust. Use it well, and you sharpen strategy, reduce pressure, and build confidence with customers and regulators alike.
And what makes that possible – keeping automation fast, accurate and risk-aligned – is context. That’s what’s going to be critical in 2026.
