Key Takeaways
- FATF identifies real estate as vehicle for 30% of laundered funds globally
- Casinos launder 15% of global illicit funds per FATF
- Trade misinvoicing accounts for 60-70% of TBML cases
- FATF: 85% countries have AML laws covering financial sector
- Global SAR filings reached 15 million in 2022 per Wolfsberg
- FinCEN processed 4.5 million SARs in US 2022
- The United Nations Office on Drugs and Crime (UNODC) estimates that 2-5% of global GDP, equivalent to $800 billion to $2 trillion annually, is laundered worldwide
- According to the Financial Action Task Force (FATF), money laundering represents approximately 3% of global GDP
- A 2023 Chainalysis report states that $22.2 billion in cryptocurrency was received by illicit addresses in 2022
- FATF: Real estate sector high-risk in 80% of jurisdictions
- Banking sector detects 60% of suspicious transactions globally per World Bank
- Casinos report 10% of global SARs per FATF
Trade-based laundering dominates, while shell companies and professional enablers drive much of the rest globally.
Common Methods
Common Methods Interpretation
Countermeasures and Effectiveness
Countermeasures and Effectiveness Interpretation
Prevalence and Volume
Prevalence and Volume Interpretation
Sectors Involved
Sectors Involved 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.
Sources & References
- Reference 1UNODCunodc.org
unodc.org
- Reference 2FATF-GAFIfatf-gafi.org
fatf-gafi.org
- Reference 3CHAINALYSISchainalysis.com
chainalysis.com
- Reference 4IMFimf.org
imf.org
- Reference 5EUROPOLeuropol.europa.eu
europol.europa.eu
- Reference 6WORLDBANKworldbank.org
worldbank.org
- Reference 7PWCpwc.com
pwc.com
- Reference 8BASELGOVERNANCEbaselgovernance.org
baselgovernance.org
- Reference 9GFINTEGRITYgfintegrity.org
gfintegrity.org
- Reference 10INTERPOLinterpol.int
interpol.int
- Reference 11HOMEhome.treasury.gov
home.treasury.gov
- Reference 12EFCCefcc.gov.ng
efcc.gov.ng
- Reference 13ENFORCEMENTDIRECTORATEenforcementdirectorate.gov.in
enforcementdirectorate.gov.in
- Reference 14COAFcoaf.fazenda.gov.br
coaf.fazenda.gov.br
- Reference 15CBRcbr.ru
cbr.ru
- Reference 16NATIONALCRIMEAGENCYnationalcrimeagency.gov.uk
nationalcrimeagency.gov.uk
- Reference 17FINTRAC-CANAFEfintrac-canafe.gc.ca
fintrac-canafe.gc.ca
- Reference 18AUSTRACaustrac.gov.au
austrac.gov.au
- Reference 19GOBgob.mx
gob.mx
- Reference 20BLOGblog.chainalysis.com
blog.chainalysis.com
- Reference 21TRANSPARENCYtransparency.org
transparency.org
- Reference 22GOgo.chainalysis.com
go.chainalysis.com
- Reference 23EUROJUSTeurojust.europa.eu
eurojust.europa.eu
- Reference 24JUSTICEjustice.gov
justice.gov
- Reference 25FINTRAC-CANAFEfintrac-canafe.canada.ca
fintrac-canafe.canada.ca
- Reference 26FINCENfincen.gov
fincen.gov
- Reference 27OPENKNOWLEDGEopenknowledge.worldbank.org
openknowledge.worldbank.org
- Reference 28ECec.europa.eu
ec.europa.eu
- Reference 29SRAsra.org.uk
sra.org.uk
- Reference 30UNCTADunctad.org
unctad.org
- Reference 31INDEXindex.baselgovernance.org
index.baselgovernance.org
- Reference 32GAOgao.gov
gao.gov
- Reference 33OECDoecd.org
oecd.org
- Reference 34WOLFSBERG-PRINCIPLESwolfsberg-principles.com
wolfsberg-principles.com
- Reference 35FINANCEfinance.ec.europa.eu
finance.ec.europa.eu
- Reference 36NICEACTIMIZEniceactimize.com
niceactimize.com
- Reference 37RISKrisk.lexisnexis.com
risk.lexisnexis.com
- Reference 38EGMONTGROUPegmontgroup.org
egmontgroup.org
- Reference 39MASmas.gov.sg
mas.gov.sg
- Reference 40STARstar.worldbank.org
star.worldbank.org
- Reference 41BCGbcg.com
bcg.com






