College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China.
Int J Environ Res Public Health. 2022 Jan 1;19(1):461. doi: 10.3390/ijerph19010461.
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.
消费者金融欺诈已成为一个严重的问题，因为它经常使受害者遭受经济、身体、精神、社会和法律伤害。识别哪些人更容易受骗，可以减轻消费者金融欺诈带来的威胁。本研究基于两阶段概念框架，整合了全国性调查中的各种个体因素（36202 名参与者），通过机器学习方法构建了欺诈暴露识别（FER）和欺诈受害识别（FVR）模型。FER 模型表现良好（f1=0.727），模型解释表明，迁移状态、财务状况、城市化程度和年龄对中国背景下的欺诈暴露具有良好的预测效果，而 FVR 模型的预测效果较低（f1=0.565），提醒我们在未来的工作中考虑更多的心理因素。这项研究为分析易受消费者欺诈影响的人群的个体差异提供了重要参考。
