Journal of Innovations in Social and Applied Sciences

Double-blind peer review · Immediate open access
Volume 2 (2026), Issue 1

Socioeconomic Inequalities in Mobile Scam Exposure in AI Era

Mohamed Abdel Alim Saber Higher Institute of Commercial Sciences, Abu Qir, Alexandria, Egypt
Mirsat Yesiltepe Department of Computer Engineering, Istanbul University, Turkey
Open Access — CC-BY-4.0
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Keywords: mobile scams; fraud exposure; socioeconomic inequality; digital financial inclusion; con- sumer protection
Abstract

Scam calls and fraudulent text messages now reach large numbers of mobile phone owners in low- and middle-income economies, yet evidence on who receives them relies mostly on complaint registers, bank records, and single-country surveys that are difficult to compare. This study describes how self-reported receipt of a scam call or text is distributed across socioeconomic groups, using respondent- level microdata from the Global Findex Database 2025. The analytical sample comprises 64,509 mobile phone owners across the 75 economies where the digital connectivity module was fielded. Weighted prevalence, percentage-point gaps, and prevalence ratios were estimated by gender, age, education, within-economy income quintile, workforce participation, and rural or urban residence, with survey weights rescaled so that each economy carried equal influence and uncertainty clustered by economy. Adjusted prevalence and average marginal effects were estimated using a weighted logistic model with economy fixed effects. Overall, 21.9% (95% CI 18.7–25.1) of phone owners reported receiving a scam call or text. Reported exposure was higher among tertiary-educated respondents than among those with primary education or less (27.4% versus 19.4%), among workforce participants than non-participants (25.0% versus 16.5%), and in the richest than in the poorest income quintile (25.2% versus 18.9%). Exposure peaked between ages 25 and 44 and was lowest at 65 and older (15.6%). Women reported 2.8 percentage points less exposure than men. After mutual adjustment, the education (+8.0 points) and workforce (+4.6 points) differences remained the largest, while the income difference narrowed to 2.6 points. Exposure to scam attempts, therefore, follows economic and communicative activity more closely than conventional markers of disadvantage, which suggests that awareness measures should reach economically active and more educated users as well as groups usually labeled vulnerable.

How to Cite
Mohamed Abdel Alim Saber, Mirsat Yesiltepe (2026). Socioeconomic Inequalities in Mobile Scam Exposure in AI Era. Journal of Innovations in Social and Applied Sciences (JISAS), 2(1), 1-17.