Psychological predictors of online fraud victimhood in China: a machine learning approach

Abstract

ABSTRACT Understanding why some individuals are more susceptible to becoming victims of fraud is crucial for developing effective anti-fraud strategies. This study employs a machine learning approach to explore the impact of individual psychological and socio-demographic characteristics on susceptibility to fraud. The random forest (RF) models reveal that psychological factors are more influential in determining an individual's vulnerability to fraud than demographic factors. Within the RF models, feature importance analyses highlight that subdimensions of critical thinking – such as truth-seeking, open-mindedness, and cognitive maturity – along with susceptibility to persuasion, perceived benefits on risk, and self-control, are pivotal in influencing an individual’s susceptibility to fraud. These insights are critical for informing targeted interventions and enhancing the effectiveness of anti-fraud measures.