Exploring the Influence of Sociodemographic Backgrounds on Money Mule Recruitment Types in South Korea: An Analysis of Probabilities from Machine Learning Classifiers

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ In South Korea, individuals who are 19 years old are legally recognized as adults and are not restricted from participating in activities allowed for adults. Typically, high school students graduate at the age of 19. Therefore, 19-year-olds are classified as equivalent to adults in their 20s.

Additional information

Notes on contributors

Sunmin Hong

Sunmin Hong is a doctoral student in the Criminology and Criminal Justice Program at The University of Texas at Dallas. His research primarily focuses on cybercrime, examining the dynamics of cyberspace and its link to criminal activities.

Changho Lee

Changho Lee is a doctoral student in Geospatial Information Sciences at The University of Texas at Dallas. His research interests lie in spatial statistics, spatial optimization, and geospatial artificial intelligence, focusing on urban issues such as environment, health, and crime.