Money Laundering Typologies 2026: 60+ Statistics from the APG Reports

Headline Money Laundering Statistics at a Glance

The fifteen figures below are the most frequently useful statistics across both reports. Each one is stated with its source document and reporting period so it can be quoted safely.

Table 2. Headline money laundering statistics. Each figure is attributed to the document it appears in: the Yearly Typologies Report 2025 [1] or Cyber Scam Hubs and Human Trafficking, May 2026 [2].

How Scam Proceeds Are Laundered: The Channel Data

APG member jurisdictions and private sector respondents were asked which channels are used to launder proceeds from cyber scam hubs. Their answers diverged sharply, and the size of that divergence is the most interesting finding in either report.

Members reported mule accounts as a primary laundering method in 76% of responses. Private sector respondents put the same channel at 45%. [2] The pattern repeats across the other two major channels, and it repeats in the same direction each time.

Figure 1. Reported use of laundering channels for cyber scam proceeds, public sector versus private sector respondents. Data source: APG Yearly Typologies Report 2025 [1].

Table 3. The public and private divergence across laundering channels. Source: APG Yearly Typologies Report 2025 [1].

Why the Gap Is Worth Taking Seriously

Part of the gap is explained by sampling. The two groups are different sizes, drawn from different populations, and answering with different definitions of what counts as primary. Any honest reading has to concede that first.

What is harder to explain away is the consistency. Three separate channels diverging in the same direction, by between 29 and 46 percentage points, is not the shape you would expect from noise alone. A more plausible reading is visibility asymmetry. A financial intelligence unit sees a mule network that spans six institutions. Each of those six institutions sees one account behaving slightly oddly.

There is a second mechanism worth naming. Mule accounts are frequently classified inside institutions as fraud rather than as money laundering, because the loss event that triggers the investigation is an authorised push payment. If a case is booked as fraud, it may never reach the typology inventory that the AML function maintains, and it will not be reported as a laundering typology when a survey arrives.

When a supervisor asks why your typology coverage looks lighter than the national picture, the honest answer is often that you are classifying the same activity under a different heading. That is a fixable problem, but only if you go looking for it. Most firms never reconcile their typology inventory against the published FSRB data at all.

Dipali Vora | AML/CFT Practitioner, AML Guild

The Named Sub-Channels

Within the virtual asset category, the reports name unlicensed exchanges, over-the-counter brokers, informal peer-to-peer platforms and crypto ATMs. Stablecoins, and USDT in particular, appear repeatedly as the settlement layer between victim funds and cash-out points in other jurisdictions. Within the remittance category, the misuse of hawala and hundi is named directly.

What the 114 Case Studies Actually Contained

The Yearly Typologies Report 2025 contains 114 case studies submitted by 17 member jurisdictions and one observer, representing over 40% of active APG members. Fifteen contributors were from Asia, one from CANZUS and one from the Pacific.

The composition of that case set is a useful proxy for what regional authorities are currently prosecuting, which is not the same thing as what is currently happening, but is the closest available measure.

Figure 2. Composition of the 114 APG case studies by predicate offence, laundering channel and payment method. Data source: APG Yearly Typologies Report 2025 [1].

Predicate Offences

Table 4. Predicate offences across the 114 case studies. Source: APG Yearly Typologies Report 2025 [1].

Types of Laundering, Channels and Payment Methods

Table 5. Laundering types, channels, payment methods and contextual factors across the 114 case studies. Source: APG Yearly Typologies Report 2025 [1].

Two observations are worth drawing out. First, banking remains the predominant vehicle at 55%, well ahead of virtual asset service providers at 11%. The narrative that crypto has displaced the banking system is not supported by the case data. Second, cash sits at 32%, three times the virtual asset share. Any list of AML typologies that treats cash as a legacy concern is not reading the same evidence.

How Investigations Start, and Who Enables the Laundering

Two findings from the 2026 report are among the most operationally useful in either document, and neither has been reported anywhere. Most investigations begin with a victim complaint rather than a suspicious transaction report, and three quarters of members identified professional gatekeepers in the laundering chain.

Table 6. How cyber scam hub investigations begin, and the professional roles involved. Source: APG Cyber Scam Hubs and Human Trafficking, May 2026 [2].

The gap between 82% and 59% is worth dwelling on. If most investigations start with a victim complaint, the regulated sector is not currently the primary detection mechanism for this crime type. The under-30% figure for members receiving an STR specifically on cyber scam hubs makes the same point more sharply.

Where Scam Hubs Operate and What Enables Them

Members reported that cyber scam hubs are typically located in urban centres, at 71% of responses, or in border areas, at 53%. These locations frequently coincide with special economic zones that combine low regulatory oversight with the infrastructure a large operation needs.

Figure 3. Enabling conditions for cyber scam hubs as reported by APG member jurisdictions. Data source: APG Yearly Typologies Report 2025 [1].

Special Economic Zones, Named

The May 2026 report names the zones directly rather than describing them generically: Dara Sakor and Henge Thmorda special economic zones in Cambodia, the Golden Triangle special economic zone in Lao PDR, and the Clark Free Port Zone Bamban in Tarlac and Cebu in the Philippines. Hubs are mainly located along border areas between Thailand, Myanmar, Lao PDR and Cambodia, along the northern Myanmar border with China, and in the Philippines. The report also notes hubs in other Southeast Asian, South American, Pacific and West African jurisdictions. [2]

On origins, the report is specific. Cyber scam hubs arose partly from the collapse of Southeast Asia's gambling industry during the COVID-19 pandemic, when casinos and hotels in special economic zones could no longer generate expected proceeds and were repurposed into scam compounds staffed by trafficking victims. The Philippines subsequently banned offshore gaming through the Anti-POGO Act of 2025, Republic Act No. 12312, which the report says had a significant and immediate impact on the prevalence of hubs there. [2]

How Trafficking Costs Are Paid

Table 7. How the costs of trafficking into cyber scam hubs are met. Source: APG Cyber Scam Hubs and Human Trafficking, May 2026 [2].

The report adds that syndicate leaders often accept a headcount alternative to a cash ransom, requiring a victim to lure two or more further victims into the compound to secure their own freedom. Where that happens, the victim becomes a recruiter, and the distinction between victim and offender that most legal frameworks depend on stops working cleanly.

Special Economic Zones

SEZs were established across Southeast Asia to attract foreign investment. The reports describe them being exploited as operational hubs because they offer reliable internet access, physical space, limited law enforcement presence and proximity to international borders that facilitates the movement of both people and funds.

Complex Corporate Structures and Gatekeepers

65% of members named complex corporate structures, including shell and front companies, as a primary means of laundering scam proceeds. [2] The reports name the professional intermediaries involved directly: lawyers, accountants, trust and company service providers, corporate formation agents and company secretaries. Shell company money laundering is not an abstraction in this dataset. In one Malaysian case study, company secretaries were used to incorporate the vehicles that made a fake investment scheme look legitimate, in an operation that accumulated close to RM 200 million, roughly USD 47.2 million. [1]

Corruption and Decentralisation

41% of members reported corrupt or complicit local authorities enabling the establishment and continued operation of scam hubs. Separately, more than one third of members surveyed reported encountering decentralisation, meaning the fragmentation of large operations into smaller mobile units that relocate across areas within cities or regions to reduce the risk of a single large enforcement action.

Law Enforcement Capability Gaps

Only 12% of APG members, meaning two of seventeen jurisdictions, considered themselves successful at tracing and seizing proceeds laundered from cyber scam hubs. The majority could not provide an estimate of the total value laundered in their jurisdiction at all.

Figure 4. Reported law enforcement challenges and self-assessed asset recovery capability. Data source: APG Yearly Typologies Report 2025 [1].

Table 10. Law enforcement challenges and self-assessed asset recovery capability. Source: APG Yearly Typologies Report 2025 [1].

The sample is small and the assessment is self-reported, and both caveats should travel with the figure wherever it is quoted. But a self-assessed capability measure is informative in a way that an outcome measure is not. Confiscation statistics tell you what was recovered. A capability measure tells you whether the authority believes recovery is achievable at all, which is upstream of every resourcing decision that follows.

Set against the wider picture, the figure is consistent rather than anomalous. UNODC has estimated that countries intercept and recover less than one per cent of global illicit financial flows, as reported by the FATF. [9] The Basel Institute on Governance has put annual financial institution spending on AML compliance at around USD 206 billion for 2023. [11] Neither figure is an APG finding. The gap between input and outcome is the central unresolved problem in this field.

Jurisdiction-Level Money Laundering Statistics

The Yearly Typologies Report 2025 contains national statistics from several members that are not reproduced anywhere else in aggregate. These are the figures most useful for benchmarking a country risk assessment.

Hong Kong, China

Table 8. Hong Kong, China money laundering and deception statistics for 2024. Source: APG Yearly Typologies Report 2025 [1].

Hong Kong also reported a shift in the origin of laundered proceeds. Domestic predicate crime proceeds fell from 64.50% to 58.89% of the overall total, while proceeds linked to foreign jurisdictions rose from 35.50% to 41.11%, an increase of 6.54 percentage points in a single year.

Figure 5. Composition of laundered proceeds in Hong Kong, China by origin, 2023 compared with 2024. Data source: APG Yearly Typologies Report 2025 [1].

Chinese Taipei

Chinese Taipei reported two movements that, read together, are the clearest evidence of channel displacement in either document. The proportion of victims remitting funds via over-the-counter bank transfers or online banking fell from 69% at the end of 2023 to 20% in the period from August 2024 to February 2025. Over broadly the same window, face-to-face cash handovers by victims rose from 23% to 46%.

Figure 6. Victim payment channels in Chinese Taipei. The two reporting periods are not perfectly aligned and this should be disclosed wherever the chart is reproduced. Data source: APG Yearly Typologies Report 2025 [1].

Chinese Taipei also reclassified the online gaming industry from high risk to medium risk in its 2024 sector vulnerability assessment, having designated it high risk in the 2021 national risk assessment.

Malaysia

Malaysia's National Fraud Portal went live in April 2024 to support the National Scam Response Centre. It integrates transaction and mule account data from multiple sources, and the reported outcomes make it the closest thing in either report to a controlled experiment in pooling institutional data.

Figure 7. Reported outcomes following the launch of Malaysia's National Fraud Portal. Data source: APG Yearly Typologies Report 2025 [1].

Table 9. Malaysia National Scam Response Centre outcomes for 2024. Sources: APG Yearly Typologies Report 2025 [1]; Bank Negara Malaysia [13].

Vietnam, New Zealand and the United Arab Emirates

Vietnam reported that online fraud accounts for 57% of all cybercrime, and named centralised exchanges, decentralised finance systems, mixers, over-the-counter peer-to-peer markets, anonymous cryptocurrencies and online gambling platforms as the common crypto money laundering channels. New Zealand attributed approximately 40% of money laundering activity to scams, followed by drug crime. [1]

The UAE contribution focused on trade based money laundering. The UAE FIU strategic analysis identified continued use of fictitious or manipulated trade documents and shipping-related fraud, with surveillance concentrated on foodstuffs, building materials, electronic products, auto parts, dual-use materials, and precious metals and stones, particularly gold. [6] The same material notes attention to trade-based terrorist financing, trade sanctions evasion, customs duty evasion, unlicensed hawala and service-based money laundering. Related UAE FIU work on organised financial fraud [7] and the UAE national risk assessment [8] are cited separately within the APG report.

How to Use These Money Laundering Typologies in Practice

Typology data is only useful when it changes something. The three exercises below are the ones I find deliver the most value from a report of this kind, and each takes less than a day.

None of this requires new technology. It requires an afternoon with the published data and an honest look at your own inventory.

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 rather than to the APG. All links were checked on 9 August 2026.

Chart and Data Reuse

The seven charts in this article were produced by AML Guild from the published APG data. You may reproduce them free of charge, in commercial and non-commercial work, provided the attribution conditions below are met.

The underlying statistics are the property of the organisations named in the references above. AML Guild claims no rights over them. The licence granted here covers the charts and the arrangement of the data on this page only.

How to Cite This Page

Use whichever of the three formats below matches your house style. If you are citing a single statistic rather than the page as a whole, cite the primary source in the references and use this page as a secondary reference.

Table 11. Citation formats for this page and for the underlying statistics.

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.

Every statistic on this page was transcribed and checked against the APG Yearly Typologies Report 2025 itself. Claims that could only be traced to secondary coverage were removed rather than qualified. Among those removed was a widely circulated estimate of just under USD 40 billion in annual scam-centre profits, attributed in press coverage to UNODC, which appears in neither APG report. Both APG documents state only that various estimates suggest tens of billions of US dollars annually.

If you find an error on this page, write to info@amlguild.com. Corrections are made in place and recorded in the version history below.

Last updated 9 August 2026. Author: Pathik Shah, CAMS, FCA, CISA, CS, DISA (ICAI), FAFP (ICAI). LinkedIn: linkedin.com/in/shahpathik