Mule Account Typologies: Why Banks and Regulators Report Different Numbers
Pathik Shah
- Asked which channels are used to launder proceeds from cyber scam hubs, 76% of APG member jurisdictions named mule accounts. Asked the same question, 45% of private sector respondents did. The same divergence appears for virtual asset service providers, at 71% against 25%, and for unlicensed remittance, at 59% against 30%.
- Three channels, three gaps, all in the same direction. This article works through the mundane explanations first, because some of the gap really is sampling, and then argues that what remains is a measurable signal about how detection is structured inside financial institutions. It closes with the Malaysian evidence that the gap narrows when institutional data is pooled, and with what an MLRO can do about it before a supervisor asks.
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Attribution note. All figures in this article come from the APG Yearly Typologies Report 2025. The public and private sector channel percentages appear in that report's focus chapter on cyber scam hubs, which drew on over 160 questionnaire responses from public and private sector entities. Every figure was checked against the source document rather than against secondary coverage. Full sourcing appears in the references.
- What a mule account is in AML terms
- The two numbers, and the pattern across three channels
- What could explain the gap, assessed honestly
- Visibility asymmetry: the structural explanation
- The Malaysian evidence
- What the case evidence shows about mule recruitment
- What this means for your monitoring programme
- What we still do not know
- Frequently asked questions
- APG members named mule accounts as a primary laundering channel in 76% of responses. Private sector respondents put the same channel at 45%.
- The same divergence appears for virtual asset service providers, at 71% against 25%, and for unlicensed remittance, at 59% against 30%.
- Part of the gap is sampling and sector mix. What remains is best explained by visibility asymmetry: an FIU sees the network, an institution sees one node.
- A second mechanism is classification. Mule activity surfaced through authorised push payment fraud is often recorded as fraud and never reaches the AML typology inventory.
- After Malaysia pooled institutional data through its National Fraud Portal, mule accounts identified rose 65% and 71,037 accounts were disrupted in 2024.
- Indian networks moved funds onward within 10 to 20 minutes of receipt, with each operator running 8 to 12 accounts.
What a Mule Account Is in AML Terms
A mule account is a bank or payment account used to receive and forward the proceeds of crime on behalf of someone else. The account itself is usually legitimate. It passed customer due diligence, it belongs to a real person, and what makes it a mule account is how it is used rather than how it was opened.
Witting, Unwitting and Complicit
Practitioners generally distinguish three types. The complicit mule is a professional criminal who understands their role in the network. The witting mule should reasonably know the activity is unlawful and proceeds anyway, usually for a fee. The unwitting mule has been deceived, most often through a fake job offer that requires opening a salary account or handling employer payments.
The distinction matters for liability and for how an institution should exit the relationship, but it makes very little difference to detection. All three types produce broadly the same account behaviour.
Why Mule Accounts Are the Preferred Channel for Scam Proceeds
Three properties. They are fast, because funds move at payment-system speed rather than at the speed of a corporate structure. They are disposable, because a network that loses accounts to detection simply recruits more. And they sidestep customer due diligence entirely, because the diligence was performed correctly on a customer who was not, at that moment, a criminal.
That last property is the one most often missed. Mule accounts are not a CDD failure. They are a post-onboarding monitoring problem, and the two are governed by different controls, different teams and often different systems. [1]
The Two Numbers, and the Pattern Across Three Channels
APG member jurisdictions named mule accounts as a primary laundering channel in 76% of responses. Private sector respondents put the same channel at 45%. The 31 point gap is the largest single divergence in the dataset after virtual assets.
Figure 1. The divergence between public sector and private sector reported use of laundering channels for cyber scam proceeds. Data source: APG Yearly Typologies Report 2025 [1].
| Channel | Members | Private sector | Gap |
|---|---|---|---|
| Mule accounts and mule networks | 76% | 45% | 31 points |
| VASPs and cryptocurrency | 71% | 25% | 46 points |
| Unlicensed or unregulated remittance | 59% | 30% | 29 points |
Table 1. Reported use of three laundering channels, public sector against private sector. Source: APG Yearly Typologies Report 2025 [1].
The consistency is what makes this interesting. One channel diverging is a survey artefact. Three channels diverging in the same direction, by between 29 and 46 percentage points, is a pattern that wants explaining.
What Could Explain the Gap, Assessed Honestly
Before treating this as a finding, it is worth working through the explanations that would make it uninteresting. Several of them carry real weight, and any use of these figures should acknowledge them.
Different Survey Populations
The two groups are not comparable in size or composition. Seventeen public sector responses from 17 jurisdictions on one side. On the other, 156 private sector responses from 14 jurisdictions, predominantly financial institutions and payment service providers. A larger and more heterogeneous sample will naturally produce lower agreement on any single answer, simply because it contains more respondents with different vantage points.
Different Sectoral Mix
The private sector sample includes entity types that would genuinely see few mule accounts. A virtual asset service provider, a corporate service provider or an insurer has no reason to encounter a retail mule network. Their honest answer lowers the average without indicating any detection failure at all.
Different Definitions of Primary
Respondents were asked to identify primary channels. A national authority aggregating across a whole jurisdiction and an individual institution assessing its own book are answering materially different questions, even with identical wording.
Reporting Incentives
Both sides have them. Authorities have some incentive to emphasise the scale of the threat they are addressing. Institutions have some incentive not to characterise their own customer base as a primary laundering channel. Neither is dishonesty, and both push in the direction observed.
What Remains
Take all of that seriously and a gap still remains. Sampling explains dispersion; it does not readily explain a consistent directional bias across three unrelated channels. Sectoral mix explains part of the virtual asset gap, but banks were well represented in the private sample and mule accounts are squarely within a bank's field of view. Something structural is doing work here.
I have watched teams discover this gap in their own numbers and immediately assume they have a detection failure. Usually they have a classification problem instead. The activity was seen, investigated and closed, just under a different label. That is a much cheaper thing to fix, but you only find it if you go and look.
Dipali Vora | AML/CFT Practitioner, AML Guild
Visibility Asymmetry: The Structural Explanation
A financial intelligence unit and a bank are looking at the same mule network through different instruments. The FIU sees the network. The institution sees one node inside it, and that node rarely looks decisive on its own.
Figure 2. The same mule account viewed from a single institution and from a financial intelligence unit.
A Network Spanning Six Institutions Is Invisible to Any One of Them
Mule networks are deliberately distributed across banks. That is not incidental; it is the point. An institution holding three accounts out of forty sees three customers with mildly elevated turnover and no obvious relationship to one another. The pattern that makes the network obvious, which is the shared beneficiaries and the sequencing between accounts, only exists at a level of aggregation the institution does not have.
Post-Onboarding Detection Is Structurally Harder
Screening at onboarding is a comparison against known lists and stated facts. Detecting a mule is a comparison against expected behaviour over time, on a customer who was genuinely low risk at account opening. The second problem is harder and the tooling is generally weaker.
Fraud and AML Data Environments Are Separated
This is the mechanism I would put most weight on. In most institutions, the loss event that surfaces a mule account is an authorised push payment fraud. It enters the fraud system, is worked by the fraud team, and is resolved as a fraud case.
If that case never reaches the AML typology inventory, then when a survey arrives asking the AML function to identify primary laundering channels, mule accounts genuinely are not top of mind. The activity was detected. It was simply booked somewhere else.
What You Classify It As Determines What You Report
The consequence is uncomfortable but useful. Part of the 31 point gap may be a taxonomy artefact rather than a detection failure. That is a better problem to have, because reconciling two internal taxonomies is achievable in weeks, whereas closing a genuine detection gap is a multi-year investment.
Suspect your own numbers would show the same gap? An AML Guild specialist can reconcile your fraud and AML case taxonomies and show you how much of the difference is classification rather than detection. Find your expert at amlguild.com.
The Malaysian Evidence
Malaysia's National Fraud Portal went live in April 2024 and pooled transaction and mule account data across institutions. The reported outcomes are the closest thing available to a controlled test of the visibility asymmetry argument.
The portal supports the National Scam Response Centre, which acts as the primary contact point for victims. It integrates transactions and mule account data from multiple sources, allowing analysis that no single participating institution could perform alone.
Figure 3. Reported outcomes following the launch of Malaysia's National Fraud Portal in April 2024. Data source: APG Yearly Typologies Report 2025 [1].
| Measure | Reported change or outcome |
|---|---|
| Mule accounts identified by the NSRC | Increased 65% |
| Time to obtain complete fund-flow information | Reduced 75% |
| Cases escalated to the Royal Malaysian Police | Increased 41% |
| Average monthly earmarked sum in fraud cases | Increased 47% |
| Suspected mule accounts identified and disrupted in 2024 | 71,037 |
| Investigation papers opened for cheating | 9,588 |
| Freeze orders on targeted collection accounts | RM 8 million, approximately USD 1.8 million |
| Calls received on online financial scams | 95,449, of which 40,010 from victims |
Table 2. National Scam Response Centre outcomes for 2024. Source: APG Yearly Typologies Report 2025 [1].
Read against the argument above, the 65% increase is the number that matters. Those mule accounts existed before April 2024. Nothing about criminal behaviour changed on the day the portal launched. What changed was the level of aggregation at which the data could be examined, and a majority of the previously invisible accounts became visible.
That is close to a direct test. If the gap were caused by mule accounts genuinely being less prevalent than authorities believed, pooling the data would not have found 65% more of them.
Two caveats. This is one jurisdiction, and the reported figures are the operator's own. The launch also coincided with wider attention to scams in Malaysia, so some of the increase may reflect reporting volume rather than analytic capability alone.
What the Case Evidence Shows About Mule Recruitment
Recruitment into mule networks and recruitment into scam compounds are converging on the same vector: the fake job offer. That convergence has direct implications for which customers an institution should regard as exposed.
The Fake Job Offer as the Leading Vector
The APG reports fake job offers as the most common recruitment method identified by members for people trafficked into cyber scam hubs. The same mechanism procures accounts. A recruitment process that asks a candidate to open a salary account, share identity documents and complete verification checks is entirely unremarkable, which is exactly why it works.
Recruitment Through Personal Networks
The May 2026 report contains an Indian analysis of debriefings from 15 nationals repatriated from Myanmar, Lao PDR and Cambodia between 2023 and 2024. Of those cases, 73% were recruited through personal connections and 27% through social media. All involved upfront fees of INR 50,000 to 70,000, roughly USD 560 to 784, creating immediate debt bondage.
The sample is fifteen people, so this is a qualitative finding rather than a regional rate, and it should never be quoted as one. But it inverts the assumption embedded in most awareness campaigns, which are built for a social-media threat when the dominant vector in this sample was someone the victim already knew.
Figure 4. Recruitment vectors among 15 repatriated Indian nationals debriefed between 2023 and 2024. This is a small qualitative sample, not a regional rate, and that limitation must be reproduced wherever the chart is used. Data source: APG Cyber Scam Hubs and Human Trafficking, May 2026 [2], Case Study 14.
At member level rather than victim level, the 2026 report found false job and business opportunities to be the most common recruitment method, named in 71% of member responses.
Why the Fake Job Offer Works
The mechanism is that nothing about it is anomalous. Sharing identity documents, opening an account to receive salary and completing verification checks are ordinary steps in ordinary employment. Neither the candidate nor the onboarding institution has any reason to treat the sequence as suspicious, because in the overwhelming majority of cases it is not.
For an institution, this means awareness messaging aimed at warning customers about strangers online is aimed at the wrong risk. Network indicators built on shared devices and shared contact details will do more work than any campaign.
Recruiters as a Distinct Role
An Indonesian case describes the economics precisely. Person A received IDR 95,000,000, around USD 5,700, from a company in another jurisdiction, and a commission of IDR 7,500,000, roughly USD 450, per person recruited. Over 2022 she sent 19 individuals abroad, receiving USD 53,200 in total. She held no licence from the Ministry of Manpower and was convicted of human trafficking.
Recruiters are a separate, paid layer of the operation. Their accounts show a recognisable pattern: regular inbound payments from a foreign corporate counterparty, of similar size, uncorrelated with any declared business.
The recruiter pattern is the most detectable part of this whole ecosystem and almost nobody models it. Regular same-size inbound payments from one foreign entity, to a personal account, with no matching commercial rationale. That is a scenario you can write on a whiteboard in ten minutes.
Jyoti Maheshwari | AML/CFT Practitioner, AML Guild
What a Mule Network Actually Looks Like at Operational Scale
Three Indian case studies in the May 2026 report give the most detailed published picture of how a mule network runs. The numbers are worth reading closely, because they define the window any detection scenario has to operate inside.
| Measure | Reported figure |
|---|---|
| Confirmed mule accounts in one network | 312, with cumulative inflows of INR 128.2 million, around USD 1.4 million, during 2024 |
| Time from receipt to onward transfer | 10 to 20 minutes in one network; 10 to 130 minutes in another |
| Accounts handled per operator | 8 to 12 active accounts, cycled several times a day |
| Geographic spread of victims | 17 Indian states in one network; 2,677 victim complaints and 60 police reports |
| A second network | 28 arrests across nine states, INR 109.8 million, around USD 1.2 million, 97 police reports, 2,922 complaints |
| A third network | 1,248 accounts mapped, with structured withdrawals of INR 40,000 to 100,000 per card per day |
| Where account holders were recruited | De-addiction centres and low-income neighbourhoods in border districts |
| Command structure | Three tiers: foreign handler, domestic intermediary, account operator, with no direct contact between the first and last |
Table 3. Operational characteristics of three Indian mule networks. Source: APG Cyber Scam Hubs and Human Trafficking, May 2026 [2], Case Studies 24, 25 and 27.
Two details deserve emphasis. The 10 to 20 minute transfer window means that any control operating on an overnight batch cycle is not a mule control. And the three-tier structure, with no direct contact between the overseas handler and the account holder, is why arresting account operators rarely reaches the organisers.
The recruitment detail is the most uncomfortable. Account holders drawn from de-addiction centres are not criminals in any meaningful sense. They are the most exploitable people available, and an institution exiting those relationships without thought is completing the harm rather than preventing it.
What This Means for Your Monitoring Programme
Five actions follow from the argument above. None require new technology and all can be started this quarter.
- Reconcile your typology inventory against the published channel data. List the channels in Table 1, then check which appear in your inventory with a corresponding detection scenario. A channel that regulators call primary and your inventory does not mention is the first thing a supervisor will find.
- Check the fraud and AML classification boundary. Take a sample of closed APP fraud cases involving receiving accounts and establish whether any of them reached your AML typology record. If none did, you have located a substantial part of your own gap.
- Build network indicators that survive single-institution limits. Shared device fingerprints, shared contact details, rotating beneficiaries, and dormancy followed by sudden velocity all work at the level of data you actually hold.
- Model the recruiter, not just the mule. Regular inbound payments of similar size from a single foreign corporate counterparty into a personal account, with no supporting commercial rationale.
- Prepare the answer before you are asked. If your numbers differ from the national picture, evidence why. A documented explanation grounded in sample composition and classification is a far better position than a defensive one.
Questions a Supervisor May Ask
| Question | What good evidence looks like |
|---|---|
| Which FSRB typologies are reflected in your inventory? | A mapping document referencing the specific published report and version |
| How do you detect mule accounts post-onboarding? | Named scenarios with tuning history, not a policy statement |
| Do fraud cases inform your AML typology record? | A documented handoff, with volumes |
| Why is your reported channel mix different from the national picture? | An analysis of sample composition and classification, prepared in advance |
| How quickly can you freeze a receiving account? | A measured elapsed time, not a target |
Table 4. Supervisory questions on mule account typologies and the evidence that answers them.
What We Still Do Not Know
The argument in this article is a reading of survey data, not a measurement of prevalence. Three limitations are worth stating plainly.
Neither survey measures how many mule accounts actually exist. Both measure what respondents named as primary channels, which is a perception measure filtered through each respondent's own visibility and vocabulary.
There is no longitudinal data. We cannot say whether the gap is widening, narrowing or stable, because this is the first year the comparison has been possible.
The Malaysian evidence is a single jurisdiction with self-reported outcomes. It is consistent with the visibility asymmetry argument. It does not establish it.
Better measurement would look like a paired study: the same question, put to a national authority and to its supervised institutions in the same jurisdiction and period, with a shared definition. Nobody has run one, and until somebody does, this remains a well-supported reading rather than a demonstrated fact.
Frequently Asked Questions
Everything you need to know about mule account typologies, detection, and how AML Guild supports your business.
What is a mule account?
A mule account is a bank or payment account used to receive and forward criminal proceeds on behalf of someone else. The account itself is usually legitimate and passed customer due diligence normally. What makes it a mule account is how it is used after onboarding, which is why it is a monitoring problem rather than a screening problem.
What is the difference between a money mule and a money launderer?
A money mule provides the account and moves funds, often for a fee and sometimes without understanding the scheme. A launderer designs and controls the scheme. Practitioners generally distinguish complicit mules, who are professional participants, witting mules, who should know the activity is unlawful, and unwitting mules, who have been deceived, usually by a fake job offer.
How common are mule accounts in money laundering?
76% of APG member jurisdictions named mule accounts as a primary channel for laundering cyber scam proceeds. Private sector respondents put the same channel at 45%. Both figures should be quoted together, because the gap between them is itself informative about detection visibility rather than a contradiction.
Why do banks and regulators report different mule account figures?
Partly because the survey populations differ in size and sector mix, and partly because of what this article calls visibility asymmetry. A financial intelligence unit sees a network spanning many institutions. An individual institution sees one node, which rarely looks decisive alone. A further factor is classification: mule activity surfaced through authorised push payment fraud is often recorded as fraud and may never reach the AML typology inventory.
How are money mules recruited?
Fake job offers are the most common vector identified by APG members. A recruitment process that asks a candidate to open a salary account, share identity documents and complete verification checks is entirely unremarkable, which is exactly what makes it effective. Neither the candidate nor the onboarding institution has reason to treat the sequence as suspicious.
Can you be a money mule without knowing?
Yes. Unwitting mules have been deceived, most often by an employment scam that requires opening an account to receive employer payments. Lack of knowledge may affect liability depending on jurisdiction, but it does not change the account behaviour, and institutions generally cannot distinguish witting from unwitting activity from transaction data alone.
How do banks detect mule accounts?
Effective detection is behavioural and network-based rather than threshold-based. The signals that work at single-institution level include dormancy followed by sudden velocity, rapid pass-through with little residual balance, shared device fingerprints, shared contact details across supposedly unrelated customers, and rotating beneficiary sets. Static value thresholds perform poorly because mule networks are designed around them.
Why does transaction monitoring miss mule accounts?
Because most monitoring evaluates one account at a time, and a mule account viewed alone is a customer with mildly elevated turnover. The pattern that identifies a mule network exists only at a level of aggregation spanning several institutions, which no single participant holds.
Does sharing mule account data between banks actually work?
The Malaysian evidence suggests it does. After the National Fraud Portal went live in April 2024, pooling transaction and mule account data across sources, the National Scam Response Centre reported a 65% increase in mule accounts identified, a 75% reduction in time to obtain complete fund-flow information, and 71,037 suspected mule accounts disrupted in 2024. Those accounts existed beforehand. What changed was the level of aggregation.
What is APP fraud and how does it relate to money laundering?
Authorised push payment fraud occurs when a victim is deceived into authorising a payment. It matters here because it is usually the loss event that surfaces a mule account. The receiving account is a laundering node and the sending event is a fraud, so the same activity sits in two different control frameworks, which is a common source of internal classification inconsistency.
What is network graph analytics in AML?
It is analysis that treats accounts as nodes and transfers as edges, so that clusters and intermediaries become visible. It matters for mule detection because the identifying pattern is relational rather than individual. Its practical limitation is that it is only as good as the breadth of data behind it, which is why pooled national data outperforms single-institution deployment.
What happens to a mule account once it is detected?
Institutions typically restrict or exit the relationship and file a suspicious transaction report. The process has to balance disruption against tipping-off restrictions, data protection obligations, and the risk of harming a customer who was deceived. Documented exit protocols matter, because inconsistent handling is a recurring supervisory finding.
Should mule detection sit with the fraud team or the AML team?
It requires both, and the classification decision has consequences beyond org design. If mule cases are only ever recorded as fraud, they will not appear in your AML typology inventory, and your reported channel mix will diverge from the national picture for reasons that have nothing to do with detection quality.
How should mule typologies appear in a risk assessment?
As a named typology with its own inherent risk rating, its own detection scenarios, and an explicit link to the published FSRB data supporting it. Referencing the source report and version is what allows you to evidence that your inventory reflects current typologies rather than historical ones.
How fast do mule networks move money?
Very fast. In one Indian network documented in the May 2026 report, funds were transferred onward within 10 to 20 minutes of receipt; in another, within 10 to 130 minutes. Each operator handled 8 to 12 active accounts and cycled funds several times a day. Any control operating on an overnight batch cycle is not, in practical terms, a mule account control.
Who ends up as a mule account holder?
Frequently the most exploitable people available. One Indian case documented account holders recruited from de-addiction centres and low-income neighbourhoods in border districts. This matters for exit protocols: treating every mule account holder as a criminal actor is often factually wrong and can compound harm to someone who was targeted precisely because they were vulnerable.
What indicators suggest a mule recruiter rather than a mule?
Regular inbound payments of similar size from a single foreign corporate counterparty into a personal account, with no matching declared business activity. In one Indonesian case the recruiter received roughly USD 450 per person recruited, and USD 53,200 in total across 19 individuals sent abroad during 2022.
References
Primary sources are listed first. Where this article reports a figure that the APG cited from another organisation, the reference points to that organisation. Links were checked on 9 August 2026.
[1] Asia/Pacific Group on Money Laundering (2025) Yearly Typologies Report 2025. November 2025. Source of the public and private sector channel figures, the Malaysian National Scam Response Centre outcomes and the Indonesian recruiter case. The channel figures appear in the focus chapter on cyber scam hubs, which drew on over 160 questionnaire responses from public and private sector entities. apgml.org/typologies/apg-typologies-reports
[2] Asia-Pacific Group on Money Laundering (2026) Cyber Scam Hubs and Human Trafficking. Sydney, Australia, May 2026. Published 7 July 2026. Survey base: 17 public sector responses from 17 jurisdictions, 156 private sector responses from 14 jurisdictions, four blockchain analytics companies and one VASP. Includes Appendix A, Red Flag Indicators. Download the report (PDF)
[3] Bank Negara Malaysia. National Risk Assessment and AML/CFT publications, including National Fraud Portal and National Scam Response Centre reporting. amlcft.bnm.gov.my
[4] Financial Action Task Force. FATF Standards and Recommendations, including the risk-based approach and customer due diligence requirements. fatf-gafi.org
Chart and Data Reuse
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How to Cite This Page
Shah, P. (2026) Mule Account Typologies: Why Banks and Regulators Report Different Numbers. AML Guild, 9 August 2026. Available at: amlguild.com/blogs/insights/mule-account-detection-gap (Accessed: [your date]).
When citing an individual statistic, cite the originating body directly rather than this page.
Disclosure and Verification
This article was written by Pathik Shah, Founder of NIYEAHMA Consultants LLP, and reviewed by the AML Guild expert panel named at the head of the page. AML Guild has no commercial relationship with the APG, UNODC, the IMF, GI-TOC or the Basel Institute on Governance, and receives no funding from any of them. No part of this article was sponsored.
The central argument of this article is an interpretation of survey data, not a measured finding. The counter-explanations are set out in full in the section titled What could explain the gap. Readers reproducing the 76% and 45% figures should carry the note that the two survey populations differ in size and sector composition. The recruitment split of 73% and 27% comes from debriefings of 15 repatriated Indian nationals and is a qualitative finding on a very small sample. It must not be reproduced as a regional rate.
If you find an error on this page, write to info@amlguild.com. Corrections are made in place and recorded below.
Last updated 9 August 2026. Author: Pathik Shah, CAMS, FCA, CISA, CS, DISA (ICAI), FAFP (ICAI). LinkedIn: linkedin.com/in/shahpathik
About the Author and AML Guild
- Dipali Vora, AML/CFT practitioner and Associate Member of the Institute of Company Secretaries of India.
- Jyoti Maheshwari, AML/CFT practitioner, published in ACAMS Today and AMLverse.
Both reviewed this article before publication. Their comments appear in the marked callouts above and are attributed.
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