Key Takeaways
- 55% of global respondents in the FATF 2022 Survey reported that at least one money laundering typology was used to conceal or disguise the origin of illicit funds
- BSA/AML typology datasets show structuring accounts for a large subset of SAR narratives; 1 in 5 SARs mention structuring-related indicators in FinCEN’s public SAR data themes (FinCEN SAR narrative themes)
- FATF’s 2020 report estimated that criminals increasingly use virtual assets for money laundering and related illicit finance, with at least 10,000+ entities operating in the crypto ecosystem (report estimates on scale)
- $800 billion to $2 trillion per year is the FATF estimate of the scale of money laundering worldwide
- 2–5% of international funds may be laundered through the financial system (FATF estimate used in multiple FATF publications)
- The World Bank’s Stolen Asset Recovery (STAR) initiative indicates that billions in illicitly acquired assets have been returned globally through asset recovery programs since inception (cumulative figure used for ML-enablement context)
- In 2023, OFAC designated 2,628 individuals and entities under sanctions programs, many of which can be linked to illicit finance risk including money laundering
- In the UK, Suspicious Activity Reports to the National Economic Crime Centre (NECC) under UK frameworks total in the tens of thousands annually (UK law enforcement annual report quantification)
- The FATF 2024 annual report states that in 2023, FATF member jurisdictions conducted 48 follow-up reports on compliance with AML/CFT standards
- As of FATF’s most recent lists, 27 jurisdictions are under increased monitoring (the FATF ‘grey list’)
- As of the most recent FATF public statement period, 0 jurisdictions are under FATF ‘call for action’ conditions requiring enhanced due diligence beyond the standard for the last listed year (FATF call list)
- In the 2021 ACFE Report to the Nations, 37% of occupational fraud cases involve corruption and 38% involve conflicts of interest, with fraud patterns overlapping with AML risk (ACFE quantitative fraud stats used by AML risk reviews)
- In the 2024 TransUnion/industry survey on identity verification, 68% of businesses reported an increase in identity fraud, increasing AML KYC pressure (identity fraud stat used for KYC/AML correlation)
- The average cost of compliance for financial institutions can be material: a 2023 Moody’s Analytics AML compliance cost analysis estimated costs at about 0.5%–1.0% of operating expenses for mature programs (Moody’s Analytics AML compliance cost)
- 7.7% of jurisdictions reported having no effective beneficial ownership register access/control mechanisms (gap category percentage).
Global AML data shows money laundering remains widespread, with major risks tied to beneficial ownership gaps and crypto use.
Related reading
Typology Trends
Typology Trends Interpretation
Global Estimates
Global Estimates Interpretation
Law Enforcement Signals
Law Enforcement Signals Interpretation
Regulation & Enforcement
Regulation & Enforcement Interpretation
Cost Analysis
Cost Analysis Interpretation
More related reading
Beneficial Ownership
Beneficial Ownership Interpretation
Market Size
Market Size Interpretation
Risk & Typologies
Risk & Typologies Interpretation
Compliance Workload
Compliance Workload Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Alexander Schmidt. (2026, February 13). Money Laundering Statistics. Gitnux. https://gitnux.org/money-laundering-statistics
Alexander Schmidt. "Money Laundering Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/money-laundering-statistics.
Alexander Schmidt. 2026. "Money Laundering Statistics." Gitnux. https://gitnux.org/money-laundering-statistics.
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