The Imperfect ‘Perfect’ Fraudster: Exploring How Vulnerability Framing, Fraudster Identity, and Victim Attachment Shape Financial Risk Perception

Huang, Yidan ; Yao, Rui (2025) — European Journal on Criminal Policy and Research

Synopsis (AI-Generated)

This catalog-style synopsis describes a scholarly examination of the figure of the “imperfect yet seemingly perfect” fraudster, focusing on how vulnerability framing, fraudster identity, and victim attachment converge to shape perceptions of financial risk. The discussion considers how narrative frames about susceptibility, desperation, or credulity influence judgments about the legitimacy of threats and the appropriate responses within financial contexts. It traces how different depictions of the fraudster—ranging from ordinary to highly calculating, from sympathetic to predatory—interact with expectations about risk, responsibility, and preventive policy. The work surveys relevant literatures on fraud typologies, risk communication, and victim psychology to map the connections among narrative choices, identity construction, and risk appraisal. It examines how cues of vulnerability can shape protective measures or trust in financial transactions, how a fraudster’s identity influences attributions of blame and assessments of recurrence, and how the emotional or material ties between victims and their financial commitments affect perceptions of exposure and precaution. Implications for policy and research agendas are highlighted, with attention to how interpretive frames influence financial risk assessment and prevention strategies. By clarifying the roles of vulnerability, identity, and attachment in risk judgments, the discussion points to the design of risk messaging, fraud-awareness efforts, and victim-support structures. The work is positioned for policymakers, researchers, and practitioners involved in criminal policy, financial regulation, and consumer protection, providing a frame for analyzing how communicative narratives shape responses to fraud risk.

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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