Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Liuchang Xu, Jie Wang, Dayu Xu and Liang Xu
Additional contact information
Liuchang Xu: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Jie Wang: Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China
Dayu Xu: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Liang Xu: Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China

IJERPH, 2022, vol. 19, issue 1, 1-12

Abstract: 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.

Keywords: consumer financial fraud; individual factors; machine learning; fraud exposure; fraud victimization (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/1/461/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/1/461/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:1:p:461-:d:716034

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().