Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Bibliographic Data

ID15467719
AuthorsLiuchang Xu (0000-0001-7635-7266, Zhejiang A & F University, corresponding author), Jie Wang (0000-0003-3564-6808, Zhejiang A & F University), Dayu Xu (0000-0001-8055-5126, Zhejiang A & F University)
Year2022
Volume19
Issue1
Pages461-461
Publication date2022-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph19010461
PMID35010720
OpenAlexW4205340590
LanguageEN
Citations received6
References cited48

Consumer financial fraud has become a serious problem because it often causes victims to suffer economic, physical, mental, social, and legal harm. Identifying which individuals are more likely to be scammed may mitigate the threat posed by consumer financial fraud. Based on a two-stage conceptual framework, this study integrated various individual factors in a nationwide survey (36,202 participants) to construct fraud exposure recognition (FER) and fraud victimhood recognition (FVR) models by utilizing a machine learning method. The FER model performed well (f1 = 0.727), and model interpretation indicated that migration status, financial status, urbanicity, and age have good predictive effects on fraud exposure in the Chinese context, whereas the FVR model shows a low predictive effect (f1 = 0.565), reminding us to consider more psychological factors in future work. This research provides an important reference for the analysis of individual differences among people vulnerable to consumer fraud

Construct (python library · Context (archaeology · Harm · Work (physics · Computer Science · Crime Patterns and Interventions · Cybercrime and Law Enforcement Studies · Psychology · Social Psychology · Spam and Phishing Detection

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