Synopsis (AI-Generated)
Connecting Chinese and American Scam Victims examines how fraud experiences are shaped by cross-cultural and cross-border contexts, focusing on victimization in two national settings. The work surveys how digital platforms, social engineering techniques, and financial pressures converge to produce similar patterns of deception while also reflecting language, cultural norms, and regulatory environments distinctive to China and the United States. It considers victim narratives, perceived harms, and responses to scam attempts, aiming to identify both common vulnerabilities and location-specific factors that influence vulnerability, reporting, and recovery. The study is positioned within broader discussions of cybercrime, consumer protection, and the social dynamics of trust, with attention to implications for awareness, education, and support for those affected. The synopsis outlines a catalog-style entry that foregrounds a comparative framework, literature context, and considerations for methodology within cross-cultural research on victimization. It signals reflections on ethical and interpretive approaches appropriate to studying scam victims across two national systems, while maintaining a neutral, reference-oriented tone. The content is oriented toward potential contributions to criminology, sociology, digital literacy, and policy discourse by highlighting how victims in each context perceive risk, assess offers, and navigate reporting channels and remedies. The entry emphasizes practical implications for policy and practice, including consumer protection efforts, targeted awareness initiatives, and avenues for international collaboration to assist victims and deter fraudulent activity. Overall, it provides a concise reference point for researchers, practitioners, and readers seeking a balanced understanding of the human impact of scams at the intersection of Chinese and American experience, as cataloged in SSRN’s Electronic Journal collection.
Identified Gaps (AI-Generated)
Identified gaps include a limited set of four documented cases and lack of comparable court data for all China cases, restricting generalization. Reliance on victim reports, civil-forfeiture orders, and media sources may introduce bias. The analysis acknowledges possible undercounting and non-exhaustive mapping of networks, with no definitive census of scams or laundering pathways beyond the studied wallets and service providers.
Methods (AI-Generated)
The study uses on-chain tracing of scam-entry wallets through money-laundering services to exchanges, supported by documentary evidence (civil orders, press reports) and an appendix of wallet addresses. It connects Florida, California, and Chinese cases via shared intermediaries and exchanges, employing flow diagrams to illustrate cross-border linkages and the scale of operations.
Limitations (AI-Generated)
Limitations include: only a subset of inflows to scam-entry wallets is proven as scam proceeds; exchange-deposits total may over- or under-attribute to scams; potential double-counting due to cycling of transactions; China cases lack comparable court documentation; findings are not exhaustive and rely on available records, limiting generalization.
Future Work (AI-Generated)
Future work should expand the dataset to include more cross-border cases, multiple jurisdictions, and longer time horizons to better quantify scale. It should seek more complete court records and corroborating evidence from exchanges and service providers, develop standardized on-chain attribution methods, and foster international law-enforcement collaboration to improve detection, prosecution, and disruption of pig-butchering networks involved with cryptocurrency.
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.