FEDERAL TRADE COMMISSION FTC.GOV 39
using cryptocurrency as the payment method.
128
The largest share of reported cryptocurrency
losses by older adults were on investment scams, with romance scams a close second.
129
f. Identifying Differences in Fraud and Consumer Issues Affecting Older
Adults of Color
FTC research and analysis has demonstrated that different demographic communities
report different types of fraud at different rates.
130
This section seeks to identify trends for older
adults in Black, Latino, and Asian American and Pacific Islander (“AAPI”) populations.
Although the FTC does not collect race and ethnicity information directly from consumers, a
method to impute race and ethnicity using name and zip code information has been applied for
the purpose of identifying any trends in reporting.
131
Using this method, over 138,000 fraud
reports filed from 2019 to 2021 from older adults most likely to be Black, Latino, and AAPI
were identified for analysis.
132
The results show differences in the reported fraud types and
128
Adults 60 and over reported $119 million (2,796 loss reports) lost to fraud with cryptocurrency as the payment
method in 2021 compared to $22 million (784 loss reports) in 2020.
129
In 2021, adults 60 and over reported losing $42 million to investment scams using cryptocurrency as the payment
method, followed by romance scams at $40 million.
130
FTC Staff Report, Serving Communities of Color, at 41-46 (Oct. 2021), available at
https://www.ftc.gov/system/files/documents/reports/serving-communities-color-staff-report-federal-trade-
commissions-efforts-address-fraud-consumer/ftc-communities-color-report_oct_2021-508-v2.pdf.
131
The procedure used to impute race and ethnicity for this report combines information from a consumer’s first
name, surname, and home zip code, with a method known as Bayesian Improved First Name Surname Geocoding
(BIFSG). Where BIFSG could not be applied, race/ethnicity probabilities were determined using surname and zip
code (BISG) and using zip code alone where surname could not be used. Consumers were then classified according
to their largest single race/ethnicity probability, an approach called max a posteriori (MAP) classification. To check
for robustness, statistics were also computed using race probabilities as weights and threshold classification methods
to ensure that the findings broadly held across methodologies. BISG is a standard technique in fair lending analysis
for banking supervision, originally explored in Mark N. Elliott et al, Using the Census Bureau’s surname list to
improve estimates of race/ethnicity and associated disparities, Health Services and Outcomes Research
Methodology 9, no. 2. (2009). For more information on BIFSG, See Ioan Voicu, Using First Name Information to
Improve Race and Ethnicity Classification, Statistics and Public Policy, Volume 5, Issue 1 (2018) at 1-13. available
at https://www.tandfonline.com/doi/full/10.1080/2330443X.2018.1427012
.
132
Three years of reporting data were used to increase the sample size. The number of 2019-2021 reports identified
for analysis for each group were as follows: 72,235 Black, 50,435 Latino, and 15,453 AAPI. Note that this
information only includes a fraction of the older adults in these populations who experienced fraud. One study has
shown that only about 4.8 percent of the victims of mass-marketing consumer fraud complained to the Better
Business Bureau or a government agency. See Keith Anderson, To Whom Do Victims of Mass-Market Consumer
Fraud Complain? at 1 (May 2021), available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3852323
(study showed that only 4.8 percent of victims of mass-market consumer fraud complained to a Better Business
Bureau or a government entity). Further, research involving nine consumer protection matters found that heavily
Black and Latino areas complained substantially less relative to their rate of victimization after controlling for other
demographic characteristics. See Devesh Raval, Whose Voice Do We Hear in the Marketplace? Evidence from
Consumer Complaining Behavior, Marketing Science (2020), 39 (1), 168-187, available at
https://deveshraval.github.io/complaintBehavior.pdf.