Key Takeaways
- Consumers globally lost an estimated $1.02 trillion to scams in 2023
- The average loss per victim of an online scam is approximately $3,504
- Investment fraud was the costliest scam type in 2023 with losses totaling $4.57 billion
- Phishing remains the top delivery method for scams accounting for 22% of reported incidents
- Tech support scams saw a 12% increase in reported incidents over the previous year
- Social media was the starting point for 25% of all fraud reports involving a loss
- Approximately 1 in 4 people reported losing money to a scam in 2023
- Adults aged 60 and older reported the highest total losses at $3.4 billion
- Younger adults (20-29) report losing money to scams more frequently than older adults
- The IC3 received 880,418 complaints in 2023, a 10% increase from 2022
- 44% of scam victims did not report the crime to anyone
- Over 75% of organizations worldwide experienced a phishing attack in 2023
- Scammers use AI to clone voices in 25% of modern imposter scams
- Deepfake video scams increased 10-fold in the financial sector in 2023
- 70% of crypto-related scams are conducted through Telegram bots
Online scams cost consumers over a trillion dollars globally last year.
Attack Vectors
Attack Vectors Interpretation
Emerging Technologies
Emerging Technologies Interpretation
Financial Impact
Financial Impact Interpretation
Frequency and Volume
Frequency and Volume Interpretation
Victim Demographics
Victim Demographics 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.
Marie Larsen. (2026, February 13). Online Scam Statistics. Gitnux. https://gitnux.org/online-scam-statistics
Marie Larsen. "Online Scam Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/online-scam-statistics.
Marie Larsen. 2026. "Online Scam Statistics." Gitnux. https://gitnux.org/online-scam-statistics.
Sources & References
- Reference 1GASAgasa.org
gasa.org
- Reference 2IC3ic3.gov
ic3.gov
- Reference 3FTCftc.gov
ftc.gov
- Reference 4PROOFPOINTproofpoint.com
proofpoint.com
- Reference 5MCAFEEmcafee.com
mcafee.com
- Reference 6SUMSUBsumsub.com
sumsub.com
- Reference 7CHECKPOINTcheckpoint.com
checkpoint.com
- Reference 8CHAINALYSISchainalysis.com
chainalysis.com
- Reference 9AKAMAIakamai.com
akamai.com
- Reference 10VERIZONverizon.com
verizon.com
- Reference 11ONSons.gov.uk
ons.gov.uk
- Reference 12SCAMWATCHscamwatch.gov.au
scamwatch.gov.au
- Reference 13BBBbbb.org
bbb.org
- Reference 14FBIfbi.gov
fbi.gov
- Reference 15DARKTRACEdarktrace.com
darktrace.com
- Reference 16IDENTITYTHEFTidentitytheft.org
identitytheft.org
- Reference 17INCinc.com
inc.com
- Reference 18REUTERSreuters.com
reuters.com
- Reference 19APWGapwg.org
apwg.org
- Reference 20ZSCALERzscaler.com
zscaler.com
- Reference 21YOUMAILyoumail.com
youmail.com
- Reference 22WATCHGUARDwatchguard.com
watchguard.com
- Reference 23SOPHOSsophos.com
sophos.com
- Reference 24FCCfcc.gov
fcc.gov
- Reference 25MICROSOFTmicrosoft.com
microsoft.com
- Reference 26INTERPOLinterpol.int
interpol.int
- Reference 27CYBERSECURITYVENTUREScybersecurityventures.com
cybersecurityventures.com






