How did age, gender, and socioeconomic status influence who became a victim in the fraud?
Executive summary
Data from multiple 2024–2025 reports show that age mattered most: adults in their 30s (often 30–39) or younger adults (20–29) appear repeatedly as the age groups with the highest counts of fraud and identity‑theft reports, while other studies show seniors register high losses or frequent reports in specific states — evidence that no single age group is uniformly safest or most targeted [1] [2] [3] [4]. Reporting differences and survey methods also reveal conflicting patterns: some national surveys find “Millennials” or 30‑somethings most victimized, others flag young job‑seekers, and some local/state data emphasize senior victims and larger dollar losses [5] [2] [4].
1. Age: multiple stories beneath one headline
National summaries from the FTC and aggregators point to adults around 30–39 as the most common identity‑theft victims in recent data [1] [6]. Moody’s and industry analysts emphasize that younger adults — notably those 20–29 who are active online and job‑seeking — reported high incident counts especially for job scams and cyber‑enabled schemes [2]. At the same time, consumer surveys and state reports show seniors frequently suffer costly scams or high per‑incident losses, so age‑risk varies by scam type and measurement: younger people report more incidents overall, while older people sometimes incur larger losses or appear as frequent victims in particular jurisdictions [3] [4].
2. Gender: reporting gaps and mixed signals
Available sources summarize overall victim counts and demographics without a consistent, detailed breakdown by gender across studies. Several large surveys list roughly equal gender splits in samples and note that both men and women are targeted, but the results do not produce a single, consistent gender pattern in who becomes a victim [5] [7]. Available sources do not mention a definitive national consensus that one gender is markedly more likely to be defrauded across all fraud types [5] [7].
3. Socioeconomic status: vulnerability vs. opportunity for fraudsters
Reports emphasize that fraudsters target profitable venues — for example, synthetic‑identity schemes aimed at auto loans and financial products — suggesting criminals select victims based on potential gain as much as on socioeconomic weakness [1]. Surveys showing broad infection rates (one in three or more Americans hit by scams in recent years) imply fraud reaches across income and education, and some journalists and analysts note scammers “don’t care about age, income, education” — they exploit opportunity and access [7] [8]. Available sources do not provide a single, detailed model connecting income brackets or education levels to victimization risk across all fraud types [1] [8].
4. Scam type changes the demographic story
The demographic profile of victims shifts by scam. Credit‑card and synthetic‑identity fraud shows high prevalence among adults in their 30s [1]. Job and remote‑work scams hit younger, tech‑active jobseekers more often (20–29 year‑olds) because of their online behavior [2]. Romance and investment scams — costly categories — and business‑email compromise produce higher median losses and can affect older adults disproportionately in dollar terms, according to consumer‑loss analyses [9] [3]. This means any claim like “young people are most targeted” is incomplete without naming the scam type [2] [1].
5. Methodology matters: surveys, FTC reports and industry studies don’t align
Differences in sample frames, question wording and reporting channels drive divergent headlines. Bankrate and other polls show one‑in‑three or two‑thirds of Americans reporting scams depending on timeframes and question sets; industry blogs and private surveys (IPX1031, Snappt) show variations in which generation reports the most victimization [7] [10] [5] [11]. The Moody’s piece, referencing FTC incident data, isolates job‑scam growth tied to online behaviors — a different lens than a household survey of lifetime scam experience [2] [5]. Analysts must therefore compare like‑with‑like before drawing definitive demographic conclusions [2] [5].
6. What this implies for policy and consumers
Because younger adults report many incidents while older adults sometimes incur larger losses, prevention needs to be multi‑pronged: outreach for job‑and‑online‑scams aimed at younger, digitally active groups, and protections such as simpler fraud reporting and financial safeguards for older adults who may suffer larger monetary impacts [2] [3] [4]. Industry attention to profitable vectors — like auto‑loan synthetic identities — suggests strengthening verification at lending and benefits checkpoints will reduce exploitation of higher‑value targets [1].
Limitations: The sources available contain uneven demographic breakdowns and different methods; they do not produce a single, definitive ranking of risk by age, gender, and socioeconomic status. Where a claim is absent in current reporting I note it explicitly above (for example, no source offers a conclusive national finding that one gender is consistently more victimized) [5] [7].