ABSTRACT This study explores the influence of socio-demographic characteristics on money mule recruitment types in South Korea. Analyzing data from 543 apprehended money mules, probabilities were calculated for each group’s involvement in recruitment types. Using class membership probability estimates from six machine learning methods, the research investigates the probability relationship between socio-demographic characteristics and recruitment types. Findings indicate that unemployed individuals in their 20s with intermediate education levels are more likely to be recruited through social media, while offline recruitment is prevalent among unemployed teenagers with low education levels and men over 50s with criminal records. Highly educated unemployed individuals in their 20s and 30s show a higher probability of using online job search channels. The study provides valuable insights into the complex interplay between sociodemographic factors and money mule recruitment, offering potential implications for law enforcement agencies in developing effective crime prevention strategies. Further analysis will shed light on the underlying reasons behind these results.
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