Before You Read On: Join the Association of Governance, Risk and Compliance (AGRC) for an essential webinar examining the critical threat that money mules pose to financial institutions and the broader economy. This free session on 11 February 2026 will provide compliance professionals, risk managers, and anti-financial crime specialists with comprehensive insights into one of the most prevalent facilitation methods used in modern money laundering schemes.
Can you briefly explain what a money mule is and why they are such an urgent problem?
The simplest definition of a money mule would sound like ‘a person who transfers funds on behalf of other persons’. If we break down this definition, ‘a person’ is usually a natural (individual) person, ‘the funds’ it transfers are funds derived from illegal activities, ‘transfers’ means moves electronically or physically, and ‘other persons’ are criminals i.e. criminal organisations.
Money mules are part of professional money laundering, as they carry out the second stage of money laundering called ‘layering’. Their purpose is to obscure the ill-gotten money trail by adding layers of transactions, such as wiring money to other accounts, cashing it out via ATMs, converting it to crypto, transferring it via money transmitters (e.g. PayPal) or physically moving cash within a country or across borders. While helping conduct money laundering, money mules simultaneously help successfully perpetrate predicate offences to money laundering by moving the proceeds from these crimes.
Money mules are increasingly becoming an urgent problem for both financial crime fighters worldwide and the international financial system for the following three reasons: the sharp rise of fraud and cybercrime and the necessity to quickly transfer the stolen funds; the implementation of the Instant Payments Regulation (10 seconds to carry out a transaction); and the high adaptability of money-mule recruiting strategy, which results in a constantly-mutating portrait of a modern money mule.
What specific behavioural, transactional, and network-based patterns best indicate mule activity and accounts across banking, fintech, and crypto rails?
A money mule account will reveal itself by demonstrating behaviour inconsistent with its expected legitimate use across banking, fintech and crypto rails. In other words, the customer is not what they seem, as their account will show no ‘normal’ spending and saving patterns, as a risk profile of a typical personal account would predict.
Such an account – a newly-opened, an already existing, or a re-awakened dormant one – will stand out thanks to its prevalent function as an extremely short-term ‘parking lot’ for incoming funds destined to leave the money mule account on the same day – within a matter of hours or even minutes. Incoming transfers will be structured, ‘smurfing’-style, electronic (and cash) payments coming from unrelated third parties (banking rails), instant P2P payments from various geolocations (fintech rails), or deposits from ‘mixers’/high-risk wallet addresses (crypto rails). The transactional pattern of a money mule account clearly says: the money is not here to stay. The goal is to move money as quickly as possible and to obscure the money trail as much as possible.
In the banking world, the funds will swiftly leave the account in form of rapid outbound, preferably cross-border, lacking clear business rationale transfers to other accounts, rapid cash ATM withdrawals, converting fiat money to cryptocurrency and moving it to a wallet to quit the traditional financial system, or switching from banking rails to fintech rails.
In the fintech realm, the money will either be rapidly converted into cryptocurrency or transferred further in a high-velocity P2P style. While the crypto rails universe will experience a money mule invoking the ‘cross-chain bridges’ method or moving money to non-custodial wallets.
Finally, the universal network-based pattern reveals itself if a money mule account acts as an upstream ‘funnel’ account. This means that multiple low-level money mules receive money deriving from crimes into their accounts which they then move to fewer higher-level mule accounts. The holders of these higher-level accounts move the same money to even fewer higher-level mule accounts. The transaction journey ends with a ‘mule herder’ account – the account of a mules’ criminal manager and the final destination of incriminated funds.
To bring this transaction maze to light, link analysis and shared data points have recommended themselves to be highly effective, as they help trace same beneficiaries of multiple transactions and identical contact details (such as a phone number or email address) shared between users, respectively.
In terms of digital platforms and fintech rails, device fingerprinting and IP clustering are helpful as well, and when navigating the crypto realm, watching out for interactions with darknet and/or sanctioned wallets is recommended.
What are the most common blind spots in current AML/fraud programmes that allow mule activity to persist, and how should teams redesign their detection architecture to address these issues?
The first and foremost blind spot is the rigidity of customer risk profiles designed to filter out money mules. Money mule activity successfully persists because the profile of a modern money mule is in constant change. Meaning if you define a typical money mule solely as ‘a young male person from a lower-income family’, you have already lost.
Because a grandmother in love with ‘an online Romeo’ can become an unwitting mule by transferring Romeo’s funds via her account; a middle-aged gentleman working remotely as a ‘Financial Agent’ at ‘a prestigious FinTech company’ in his spare time can in fact be money-muling for an organised criminal group (OCG); a vulnerable refugee can be tricked by an OCG into opening an account in their own name and then unwittingly surrender control of the account to that very OCG. The static character of customer risk profiles does let unconventional and new variations of money mules slip through the cracks.
The second blind spot is directly connected to the first one and reads ‘rules- and threshold-based transaction monitoring’. If you solely apply this type of monitoring, your internal safeguard measures don’t stand a chance against money mules’ sophisticated strategies, as criminal managers behind the mules will structure transactions to circumvent the traps you set in your systems.
The third blind spot is data fragmentation reflected in the lack of data sharing primarily within compliance and anti-financial crime departments, let alone front-office employees. Working in silos results in time-costly ‘nibbling’ at the same case by multiple teams without getting a full, realistic picture of a money mule who by definition sits at the intersection of AML and fraud worlds, and who might be categorically identified by a front-office employee who would raise the alarm with their compliance colleagues immediately.
To rectify the situation, your organisation’s detection architecture could be re-designed as follows. Diversifying money-mules-centric risk profiles based on the latest known and emerging typologies and clustering them would considerably widen the net to catch money mules who used to comfortably fly under your radar.
Next, you tie these newly defined profiles to your transaction monitoring systems by bringing in four dynamic enhancement features: propensity modelling, link analysis, pre-transaction real-time interdiction, more focus on inbound transactions, and cross-channel visibility.
Propensity modelling will provide you with data-driven insights indicating a customer’s propensity for falling for easy-money job ads (e.g. account overdraft, significant decrease in spending, credit-card overuse all point to financial distress and the need to quickly find a source of active income). Link analysis will lay bare hidden money mule networks and point to criminal managers ultimately aggregating incriminated monies. Pre-transaction real-time interdiction will create strategic friction and automated holds in executing transactions, if transactional behaviour of the customer deviates from their high-confidence behavioural baseline.
Focusing on inbound transactions will alert you in terms of the business volume of money mules’ activity due to their ‘receiver’ role. While cross-channel visibility will give you a roadmap to the customer’s logic of moving funds in terms of using ATM cash withdrawals, conversion to cryptocurrency or involving money transmitters’ services (e.g. PayPal). Combined, these measures will create a holistic view of the customer’s transactional behaviour and enable your organisation to detect quicker and ultimately react faster.
Finally, you enrich your existing knowledge of the customer with new and unique intelligence brought to you by your fellow teams and departments who monitor your customer from different perspectives and whose insights can help in spotting money mules and reporting them faster.
Also, keep in mind the benefits of behavioural analytics, as device profiling/fingerprinting and IP clustering will provide you with valuable clues for customer’s suspected money mules activities. It goes without saying that enhancing fraud analytics at onboarding will make it harder for money mules to get an entry ticket into your financial organisation.
Are there any specific legal, privacy, or other considerations compliance teams should pay close attention to when redesigning these processes?
My answer is a definite yes. As for specific legal considerations, the EU regulatory landscape (EU AML Package including 6AMLD) is definitely on our side in terms of proactively addressing the money mule problem.
Acting as a money mule translates into aiding and abetting money laundering – something required to be criminalised. This gives you the green light to build enhanced detection, defences, and enhanced reporting of money mules. Let’s see how the AMLA supervision, which is designed to bring uniform detection standards, will unfold in the coming years.
As for privacy considerations, your organisation obviously needs to be compliant with the GDPR. In the sense that GDPR-based data minimisation and purpose limitation requirements must be reconciled with intensive and extensive use of personal data required to detect money mules both at onboarding and during customer lifecycle.
This is solved by GDPR Article 6 (1) (c) allowing processing of sensitive data due to legitimate interest (legal obligation based on AML concerns) on the part of financial organisations. Hence, the green light is given here as well, just remember to balance out the extent of collecting personal data with the data privacy rules.
Needless to say, documenting your approach is key to prove to your National Competent Authority (Regulator) that, though your organisation is going an extra mile to effectively fight money mules, it is doing so in a responsible, law-abiding manner.
Regarding any other considerations, I would suggest performing a Data Protection Impact Assessment (DPIA), if your organisation decides to deploy new software designed to assess customers’ online behaviour to detect any red flags. This exercise serves the goal of documenting the necessity of infringement on customers’ privacy justified by security considerations.
What kind of external collaboration or practical operational controls could help prevent mule recruitment and the use of genuine customers in this type of illicit activity?
Effectively fighting the money mules phenomenon requires sustained and collective efforts on the part of multiple actors from both outside and within the organisation. The three pillars of the external collaboration are formed by Public-Private Partnerships (PPPs), cross-sector collaboration, and targeted prevention though education.
While Europol’s EMMA (European Money Mule Action) campaign (PPP) helps catch thousands of money mules and money recruiters each year, the Anti-Financial Crime Alliance (AFCA) in Germany (PPP) enables strategic exchange of information on new typologies between the private sector, supervisory authorities, and law enforcement respectively, whereas the Joint Money Laundering Intelligence Taskforce (JMLIT) and National Economic Crime Centre (NECC) in the UK (also PPP) allow intelligence exchange about active money mule rings.
Cross-section collaboration with social media and other online platforms (such as LinkedIn) helps weaken money mules herders’ recruitment attempts, such as taking down fake easy-money job ads (‘Financial Agent’). Finally, targeted prevention through tailored customer education is specifically directed at population groups which money mule herders will try to exploit most: youth, side-hustlers, refugees and migrants, as well as elderly persons.
Within a financial organisation, the following three practical operational controls would curb mule recruitment and exploitation of unwitting genuine customers: Verification of Payee (VoP), invoking strategic friction and ‘cooling-off’ phases, and questioning by front-line staff.
VoP proactively helps disrupt fraudulent social engineering attacks against unwitting customers. Strategic friction is designed to refrain customers from being drawn into money muling, whereas deliberate ‘cooling-off’ phases give genuine customers time to reconsider their decision to send money via their account because their “online sweetheart is in sudden dire need.” The questioning by front-line staff brings in the strong element of human interaction and is designed to help unwitting customers come to their senses.
What are your top three recommendations for compliance teams to reduce mule activity?
My first recommendation would be to concentrate more on your customers by adopting 360-degree monitoring of their identity and behaviour. Strengthen onboarding, diversify risk profiles, merge traditional KYC information with behavioural biometrics, device fingerprinting, and geolocation data. Invoking the ‘Identity-First’ approach will enable you to keep new, willing-to-onboard money mules at bay and spot both freshly converted and about-to-convert mules among your existing customers.
Second, elevate your transaction monitoring in the ways it truly serves its purpose to proactively detect, stop, and report money mules’ activities. Consider incorporating machine learning elements to make your transaction monitoring a truly data-driven one.
Third, break the silos for good. Work tirelessly on building constructive, high-quality, long-term dialogue with your AML, fraud prevention, transaction monitoring, special investigations, regulatory response, and front-office colleagues. Because together you will be strong enough to make your organisation’s money mule front invincible.
Natalie Detsik is a strategic financial crime prevention leader driving enterprise-wide risk management, regulatory alignment, and audit readiness across complex organisations. Expert in designing compliance frameworks, optimising transaction monitoring, and guiding AML & Fraud strategy through data-driven insights. Recognised for fostering governance, enhancing oversight structures, and enabling cross-functional collaboration that strengthens regulatory resilience. Trusted advisor to senior stakeholders, delivering actionable guidance on policy, onboarding, and new product risk. Influences change by translating evolving regulations into practical safeguards that protect operations, reduce exposure, and support sustainable compliance growth. An energetic, outward-looking and mindful leader who combines both genuine empathy and intercultural awareness and whose working style is marked by executive leadership and can-do-attitude.
