A new type of victim? Profiling survivors of modern slavery in the online scam industry in Southeast Asia

Franceschini, Ivan ; Li, Ling ; Hu, Yige ; Bo, Mark (2024) — Trends in Organized Crime

Synopsis (AI-Generated)

This catalog entry examines the emergence within the online scam economy in Southeast Asia of a particular victim group—survivors of modern slavery who become entwined with organized illicit networks. It surveys how these individuals are identified, categorized, and understood within the broader ecosystem of digital fraud, with emphasis on the social, economic, and technological conditions that shape recruitment, coercion, and participation. The discussion highlights the interaction between personal histories of exploitation and structural factors such as labor markets, migration patterns, platform governance, and the governance structures that sustain illicit activity. The material considers a range of settings—from regional hubs to cross-border operations—emphasizing how varying jurisdictions, languages, and regulatory contexts intersect with the dynamics of the online scam industry. The synopsis outlines recurring themes in the literature on victims, exploitation, and organized crime in this context, including power relations, control mechanisms, and the role of digital tools in facilitating fraudulent activity. It notes conceptual approaches to profiling and indicators, as well as the challenges of studying clandestine economies through high-level analysis. The entry also points to potential implications for policy and practice, including prevention efforts, survivor support, and coordination among law enforcement, labor-rights organizations, and digital platforms. Overall, the topic is framed as a lens on vulnerability and resilience within evolving cross-border criminal networks, offering a neutral overview of how survivor experiences intersect with the structure and operation of the modern scam economy in Southeast Asia.

AI-Generated Content Notice

The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.



        
      

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