| POPULATION | COMPLAINTS | AVG LOSS | MEDIAN LOSS | TOTAL LOSSES | RELIEF RATE | % OF TOTAL LOSSES |
|---|---|---|---|---|---|---|
| Older Americans | 42 | $155,379 | $10,000 | $4.82M | 2.4% | 37.5% |
| Servicemembers | 70 | $5,962 | $2,450 | $321K | 8.6% | 3.3% |
| Older Servicemembers | 8 | $15,134 | $2,000 | $75.7K | 12.5% | 0.8% |
| General Public | 559 | $16,543 | $1,700 | $7.69M | 9.7% | 72.6% |
| FRAUD TYPE | OLDER AMERICANS n=42 · avg $155,379 · med $10,000 |
SERVICEMEMBERS n=70 · avg $5,962 · med $2,450 |
GENERAL PUBLIC n=567 · avg $16,543 · med $1,700 |
|---|---|---|---|
| Business Impersonation | 38.1% (16) | 50.0% (35) | 54.9% (311) |
| Investment Scam | 31.0% (13) | 18.6% (13) | 12.5% (71) |
| Online Shopping / Marketplace | 16.7% (7) | 18.6% (13) | 17.6% (100) |
| Government Impersonation | 2.4% (1) | 4.3% (3) | 4.1% (23) |
| Romance Scam | 4.8% (2) | 0.0% (0) | 0.4% (2) |
| Job / Task Scam | 0.0% (0) | 1.4% (1) | 2.3% (13) |
| Other / Unclassified | 7.1% (3) | 5.7% (4) | 8.6% (49) |
| Relief Rate | 2.4% | 8.6% | 9.7% |
| Institution | Count | Share |
|---|
adults 60+, 2024
per incident
highest of all groups
just 5% of cases ($100K+)
⚠ AI-Assisted Analysis Disclaimer
This dashboard was produced with AI-assisted analysis. While all figures have been cross-verified against primary source documents, AI models can make errors in data extraction, classification, and arithmetic. Readers are encouraged to verify key figures against the original CFPB and FTC source files before citing. Any inaccuracies are the responsibility of the analyst.
Data Source — CFPB
Primary dataset: CFPB Consumer Complaint Database, filtered to product category "Money transfers, virtual currency, or money services" with issue type "Fraud or scam." Period: March 4, 2025 – March 4, 2026. n = 679 complaints.
The CFPB database is not a representative sample of all fraud in the US economy. It functions as a lightning rod for high-value financial disputes — complaints involving regulated banks, wire transfers, and money service businesses. Low-dollar scams, cash-based fraud, and peer-to-peer losses without a regulated intermediary are systematically underrepresented. Results should be read as a lower bound and a snapshot, not a complete picture.
Source: consumerfinance.gov/data-research/consumer-complaints/
Data Source — FTC
FTC comparison figures are drawn from Protecting Older Consumers 2024–2025: A Report of the Federal Trade Commission (December 1, 2025), Section IV.A, pp. 18–27. Data reflects full calendar year 2024 Consumer Sentinel reports for adults ages 60+, n=421,031 age-identified reports. All FTC figures were verified directly from the primary source PDF.
Source: ftc.gov/reports/protecting-older-consumers-2024-2025
Fraud Classification
Each complaint was classified using a keyword-matching algorithm aligned to the FTC's official fraud taxonomy. Primary classification assigns each complaint to exactly one category; co-occurrence analysis allows multiple matches. Classification was performed computationally on complaint narratives with a manual audit of 204 multi-transaction complaints.
Three complaints containing the CFPB's {>= $1,000,000} redaction format were manually identified and corrected after the automated extraction script failed to parse the non-standard notation.
Loss Figures & Underreporting
Total identified losses: $12.8M across 550 of 679 complaints (81.0%) with parseable dollar amounts. Mean loss: $23,329. Median loss: $2,000. The underreporting multiplier (2–6.7%) is derived from FTC research, yielding an estimated true loss range of $191M–$641M.
Losses for the three {>= $1,000,000} complaints are recorded at $1,000,000 (conservative floor). One complaint (ID 18036836) was corrected from $690K to $1,210,000 based on two wire amounts in the narrative. One complaint (ID 17104270) was corrected from $800K to $290,000 after review identified $800K as an unverified platform balance.
Vulnerable Population Tagging
CFPB complaints include optional consumer self-identification tags for "Older American" (age 62+) and "Servicemember" (active duty, veteran, or military family). These tags are voluntary and self-reported, leading to systematic undercounting. Population figures should be treated as lower bounds.