A Survey of Scam Exposure, Victimization, Types, Vectors, and Reporting in 12 Countries

TL;DR

This study fills a critical data gap by delivering a nationally representative, cross-country survey of scam exposure, victimization, types, vectors, and reporting across 12 countries (n = 8,369). Using weighted heatmaps and Spearman correlations, it reveals that lower-income contexts face higher loss rates, that the internet underpins most scam activity, and that reporting remains markedly under-developed, especially in less affluent settings. The findings show that money-making and online-shopping scams are pervasive across economies, with affordance of digital payments and online channels driving victimization in many regions. The paper discusses intervention strategies—ranging from awareness campaigns and nudges to inoculation and financial/digital literacy—to bolster resilience and improve reporting, offering actionable guidance for policymakers, practitioners, and researchers.

Abstract

Scams are a widespread issue with severe consequences for both victims and perpetrators, but existing data collection is fragmented, precluding global and comparative local understanding. The present study addresses this gap through a nationally representative survey (n = 8,369) on scam exposure, victimization, types, vectors, and reporting in 12 countries: Belgium, Egypt, France, Hungary, Indonesia, Mexico, Romania, Slovakia, South Africa, South Korea, Sweden, and the United Kingdom. We analyze 6 survey questions to build a detailed quantitative picture of the scams landscape in each country, and compare across countries to identify global patterns. We find, first, that residents of less affluent countries suffer financial loss from scams more often. Second, we find that the internet plays a key role in scams across the globe, and that GNI per-capita is strongly associated with specific scam types and contact vectors. Third, we find widespread under-reporting, with residents of less affluent countries being less likely to know how to report a scam. Our findings contribute valuable insights for researchers, practitioners, and policymakers in the online fraud and scam prevention space.

Figures (6)

• Figure 1: Weighted within-country proportions of responses to Q1: In the past year, have you been the target of a scam? Darker colors gradually indicate higher values. The top row describes the percent who lost money among those who experienced a scam; it is computed from the two rows immediately below it.
• Figure 2: Weighted within-country proportions of responses to Q2: What kind of scam were you targeted by? Choose the one that most accurately describes your experience. Darker colors gradually indicate higher values.
• Figure 3: Weighted within-country proportions of responses to Q3: How did it start (e.g., how did they first contact you, where did you see an ad)? Darker colors gradually indicate higher values.
• Figure 4: Weighted within-country proportions of responses to Q4: How did you pay or send the money? Darker colors gradually indicate higher values.
• Figure 5: Weighted within-country proportions of responses to Q5: Did you report the scam to anyone? Darker colors gradually indicate higher values.