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Romance Scams

Shepherd, Lynsay A. ; Szymkowiak, Andrea (2026) — The Palgrave Encyclopedia of Cyberpsychology

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DOI: 10.1007/978-3-031-52643-5_173-1 (opens in a new tab)

Country:   Global
Tags:   AI-Enabled Fraud, Cybercrime, Deepfakes, Online Dating, Prevention, Romance Scams, Social Engineering
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Synopsis

            
The publication provides a concise, source-grounded overview of online romance scams, tracing their evolution over roughly two decades into a multifaceted cybercrime. It frames romance scams as social-engineering exploits that increasingly rely on technology, including AI-generated profiles, deepfakes, and other advanced tools, to establish seemingly authentic relationships and then solicit money through feigned emergencies or coercive tactics. The text notes that reports of romance scams have risen in recent years and cites publicly available figures such as a 20% increase reported by Barclays in 2025 and the FBI’s IC3 data on financial losses, while explicitly acknowledging these statistics as reference points rather than universal claims. A central theoretical touchstone is Whitty’s seven-stage Scammers Persuasive Model, which documents a progression from initial online dating to grooming, a sting or emergency, ongoing manipulation, and revictimization. In terms of approach and implications, the publication surveys prevention and detection efforts that blend education, law enforcement, industry action, and technological interventions. It highlights AI- and ML-enabled defenses, such as automated profiling tools and deepfake detectors, and notes interactive education approaches, including VR-based training (HeartGuard VR) and other interactive platforms. It emphasizes cross-sector collaboration among academia, police, platforms, and charities as essential to mounting a coordinated response, given the global and evolving nature of the threat. The piece points to gaps in mitigation, particularly around preventing revictimization, and it presents a cautious, multi-pronged call for continued research, shared intelligence, and integrated policy efforts to reduce harm.

          

        
                  

            
Identified Gaps

            
The publication identifies comparatively limited research on technological interventions for detecting romance scams or educating users. It also notes that few studies address mitigation of revictimization, indicating a need for research supporting victim recovery. Seasonal, text-based awareness campaigns may not engage users effectively or reach people who do not identify with the term “romance scam.”

          

        
                  

            
Methods

            
This encyclopedia entry provides a narrative synthesis of published research, models, statistics, prevention campaigns, detection tools, and partnership approaches concerning online romance scams. It describes Whitty’s staged persuasion model and summarizes examples of machine-learning detection, virtual-reality education, and cross-sector collaboration. It does not report original data collection or a systematic review protocol.

          

        
                  

            
Limitations

            
As a broad encyclopedia overview, the publication does not provide original empirical data, a reproducible search strategy, or comparative evaluation of the interventions it describes. Several claims about effectiveness are framed as potential benefits or recommendations, and the entry notes that scam reports continue to rise despite available tools.

          

        
                  

            
Future Work

            
Develop and evaluate technological detection and educational interventions, particularly interactive approaches. Study and mitigate revictimization and support victim recovery. Improve cross-sector, international intelligence sharing, continuous monitoring, and coordinated prevention. Research should identify vulnerability factors, classify fraudulent behavior, and examine offender contexts using publicly available and newly generated data.

          

              

    
    

        
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