Pig Butchering Scams as Cyber-Enabled Financial Crime: A Scoping Review of Dimensions, Modus Operandi, and Victim-Offender Dynamics

Gujarathi, Palak ; Verma, Shankey ; Nair, Vipin Vijay (2026) — Deviant Behavior

Synopsis (AI-Generated)

This piece examines Pig Butchering Scams as Cyber-Enabled Financial Crime: A Scoping Review of Dimensions, Modus Operandi, and Victim-Offender Dynamics within the broader context of online fraud and mediated communication. It outlines common patterns documented in the literature, describes how offenders cultivate trust and shift interactions onto controlled channels, and notes the role of staged identities, persuasive scripts, and escalating commitment. The discussion situates these elements within themes frequently reported by victims, including emotional grooming, urgency cues, and isolation from outside advice. The work also highlights typical areas of inquiry for researchers and practitioners: factors associated with victim susceptibility, the influence of platform affordances, and touchpoints where prevention or disruption is most feasible. Attention is given to reporting barriers, financial harms, and downstream impacts on wellbeing. Implications emphasize the value of cross-sector collaboration, clearer platform policy enforcement, and targeted awareness strategies informed by real case dynamics. Presented in Deviant Behavior, the piece contributes to ongoing efforts to translate observed scam mechanics into actionable guidance for detection, education, and support.

Identified Gaps (AI-Generated)

Explicit gaps include limited offender-focused analyses (recruitment pathways, syndicate hierarchies, laundering infrastructures) and incomplete understanding of cross-border regulation and enforcement. The literature also underexploits prevention, victim-support, and long-term recovery strategies, and lacks integrative, cross-disciplinary frameworks linking criminology, cybersecurity, and financial regulation. Additional work is needed on cross-jurisdictional coordination, data-sharing, and empirical data from diverse regions to map transnational networks and their operational dynamics.

Methods (AI-Generated)

Design/methods: JBI-guided scoping review, PRISMA-ScR. Searches of four databases (Scopus, EBSCOhost, Taylor & Francis Online, Google Scholar) for English-language studies 2020–2025. From 119 unique records, 12 studies met inclusion. Data extraction followed the JBI framework; analysis used reflexive thematic analysis, yielding five themes: dimensions, modus operandi, victim characteristics, offender characteristics, outcomes. No formal quality appraisal due to scoping scope. Data sources included interviews, court records, scam scripts, and blockchain forensics.

Limitations (AI-Generated)

Limitations include absence of formal risk-of-bias appraisal and reliance on heterogeneous, narrative data across designs. Language and time restrictions (English, 2020–2025) may omit relevant work. Varied depth of reporting, potential publication bias toward scholarly sources, and uneven geographic coverage may affect comprehensiveness. Cross-database overlap and no meta-analysis are additional constraints.

Future Work (AI-Generated)

Future research should broaden offender-centric analyses (pathways, organizational structures, and laundering networks) and quantify cross-border regulatory and enforcement gaps. Develop integrative theoretical frameworks across criminology, cybersecurity, and financial regulation; evaluate prevention and victim-support interventions; establish standardized datasets and cross-jurisdiction data-sharing; pursue longitudinal studies on recovery and reintegration; assess regulatory responses and enforcement effectiveness; examine AI-generated content and countermeasures to mitigate manipulation and deception.

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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