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Banks can reduce the chance of opening accounts that will be used to move criminal funds by combining application-fraud screening, identity checks required by applicable rules, and intelligence that connects applications to payment and network activity. No single onboarding signal proves that an applicant is a money mule: a person may be knowingly involved, unaware of the scheme, or coerced. The right aim is to identify risk early, then investigate and respond proportionately.
What a money mule account is—and why identity checks are not enough
The UK government defines money muling as moving the proceeds of crime on behalf of criminals, sometimes in return for money or another benefit. Funds may pass through personal accounts, cryptocurrency transactions, or cash withdrawals and handovers. A person may knowingly take part, be recruited without understanding the criminal purpose, or be manipulated or coerced. The government cautions against treating “money mule” as a label for victims of financial exploitation because it can stigmatise and dehumanise them. UK government guidance on money laundering-linked financial exploitation and the FBI’s explanation of money mules describe these different circumstances.
The FBI describes unwitting recruits approached through romance schemes or job offers, people who ignore warning signs, and people who knowingly open multiple accounts, operate funnel accounts, or recruit others. A recruit might be asked to use an existing account or open a new one in their real name. That is why confirming that an applicant is who they claim to be is important, but cannot by itself show how the account will be used or establish the applicant’s intent.
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Onboarding controls can assess the application and identity information available at the time, and can compare it with relevant fraud or risk intelligence. But the cited official guidance does not establish a universal list of mule-account features, a validated score, or a threshold that identifies a mule with certainty. Nor does it support treating a clean credit file or an ordinary-looking banking history as proof that an account is safe: the UK National Crime Agency (NCA) notes that criminals may recruit people with no adverse credit history and existing accounts with no reported problems. The NCA’s February 2022 banking-sector feature describes application-fraud screening as one way banks seek to prevent mule accounts being opened, not as a guarantee.
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In practice, an alert is a reason to assess the application in context—not a finding that the customer is guilty or has knowingly agreed to move criminal money. Some evidence only becomes visible after transactions begin, or when activity across multiple accounts is connected. Onboarding decisions therefore work best as one layer in a broader detection and review process.
How to build a layered detection approach
The NCA describes several controls used in the UK banking sector. The table distinguishes the point at which each type of control can help; it does not prescribe a particular vendor, algorithm, or required implementation.
| Control | When it helps | What it can reveal | Important limit |
|---|---|---|---|
| Application-fraud screening | During account application | Risk in the application that may warrant review before an account opens | A clean credit history or existing account is not proof of low mule risk. |
| Identity verification | During customer checks | Whether identity information meets the applicable verification requirements | Verified identity does not establish that future account activity will be legitimate. |
| Inbound and outbound payment profiling | As payments occur | Patterns in money entering or leaving an account | Activity may need to be assessed over time and in context. |
| Industry intelligence feeds | During screening and ongoing monitoring | Relevant information shared across the industry | The NCA describes the category, not a specific feed or required match threshold. |
| Analytics to identify mule rings and profiling of mule activity | Across accounts and over time | Connections or patterns that may not be apparent from one application alone | The NCA does not specify a required analytic method or claim that a pattern proves intent. |
These categories are described in the NCA UK Financial Intelligence Unit’s February 2022 “Money mules in the banking sector” feature. A practical interpretation is to use application screening to identify cases for attention, then connect relevant onboarding information to payment monitoring and wider intelligence. Controls focused only on an individual application may miss links that emerge between accounts or later activity; monitoring only after payments start forfeits the opportunity to review risk before opening.
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Apply identity checks under the rules for the relevant jurisdiction
Identity verification requirements depend on where the bank operates. For the UK, HM Treasury and the Department for Science, Innovation and Technology published “Using digital identities with the Money Laundering Regulations” on 26 February 2026. It says regulated entities can use digital verification services for relevant customer due-diligence checks under the Money Laundering Regulations, provided the service is listed on the GOV.UK register of services certified against the UK digital verification services trust framework. The guidance supplements rather than supersedes the regulations; it concerns identity checks and does not say that a certified service detects money mules. Banks elsewhere should apply the identity rules that govern their own jurisdiction.
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Review warning signs without turning them into automatic verdicts
UK government guidance on financial exploitation lists possible signs including unexplained deposits, changes in banking behaviour, new accounts, changed cash or ATM use, and secrecy about finances. These signs are not specific to money laundering-linked exploitation. A bank may also have difficulty distinguishing a complicit participant from someone who is being manipulated or coerced. Use signals to guide proportionate investigation and, where appropriate, safeguarding—not demographic profiling or automatic denial based on one indicator.
That distinction matters when choosing a response. An account restriction or freeze can leave an exploited person without access to money and may deepen their instability. Review processes should take the quality and context of available evidence into account and leave room to recognise potential coercion, rather than treating every alert as proof of criminal intent. The UK government’s frontline guidance discusses both the indicators and the risks of harm to people experiencing financial exploitation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What UK indicators say about the scale of the problem
The UK government’s financial exploitation guidance describes money laundering as occurring at a scale of “hundreds of billions of pounds” annually in the UK and estimates that more than £10 billion in illicit funds is laundered through UK money mule networks each year. The page does not specify the publication year for the underlying estimates in its available text, so they should be read as attributed estimates rather than current audited totals.
The same guidance reports that more than 39,000 accounts demonstrated behaviour indicative of money muling in 2022. That is an indicator count, not a count of proven criminal accounts. It also cites Cifas and UK Finance reporting that 23% of accounts reported as bearing hallmarks of money-muling activity in the first six months of 2023 were owned by people aged 21 and under. That figure describes reported indicators and does not establish that every account holder knowingly participated.
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Account for emerging AI risks without overstating them
The UK’s 2025 National Risk Assessment of Money Laundering and Terrorist Financing says current use of AI for money laundering is not fully understood and is not believed to be widespread at present. It reports engagement between the private sector and law enforcement indicating uses that include synthetic bank-account creation, fraud and impersonation, phishing, and mule onboarding; it also discusses potential future misuse, including generated synthetic identities and efforts to evade anti-money-laundering defences. This is a qualified risk assessment, not evidence that AI-generated mule accounts are already widespread or a basis for treating an AI-related signal as proof of wrongdoing.
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