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The scammer’s playbook: Exploring the psychological techniques and tactics used by scammers in the social engineering of cryptocurrency fraud

Brandon Dulisse ; Chivon Fitch ; Nathan Connealy (2026) — Journal of Economic Criminology

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DOI: 10.1016/j.jeconc.2026.100211 (opens in a new tab)

Country:   United States
Tags:   Cryptocurrency Fraud, Financial Loss, Fraudulent Trading Platforms, Pig Butchering, Psychological Tactics, Psychological Techniques, Social Engineering, Victim Narratives
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Synopsis

            
This study analyzes how scammers use psychological tactics and techniques to conduct cryptocurrency-related social engineering, drawing on 282 victim narratives from California and Wisconsin crypto scam trackers collected during 2023–2024. The researchers adapt a framework that separates psychological tactics (PTacs) and psychological techniques (PTechs) to code the scammers’ messaging and interactions in the narratives. The analysis focuses on the two dominant scam types in the sample—fraudulent trading platforms and pig-butchering schemes—and finds that impersonation and persuasion are the most common techniques, often paired with familiarity and form- fitting narrative elements that establish credibility and trust. Across all cases, the scammers deployed an average of about 1.77 tactics and 1.86 techniques per incident, with higher psychological complexity (the combination of multiple tactics and techniques) linked to larger financial losses in fraudulent trading platform scams. Pig-butchering scams also showed substantial technique use, although losses varied and were not always statistically distinct from other types in every comparison. The results suggest that crypto fraud is driven less by random opportunism and more by a repeatable, structured “playbook” of social engineering that can be adapted to different platforms and victim contexts. The authors argue that prevention should go beyond generic warnings and toward targeted interventions that disrupt scripted interactions, enhance psychological literacy, and improve platform design and reporting. They propose real-time pattern recognition in scam trackers and standardized narrative fields to identify rising scam scripts, enabling regulators and platforms to issue earlier, more precise alerts. Overall, the work portrays cryptocurrency scams as organized, scalable social-psychological campaigns with identifiable patterns that are amenable to proactive disruption.

          

        
                  

            
Identified Gaps

            
The paper identifies an understudied need to measure psychological tactics and techniques in cryptocurrency scams using victim self-report narratives. It also notes that less is known about the repeatable scammer “playbook” and that small samples prevented loss comparisons across most scam types.

          

        
                  

            
Methods

            
The study coded 282 publicly available, reviewed consumer-complaint narratives from California and Wisconsin cryptocurrency scam trackers collected through October 2024. Researchers applied seven psychological tactics and seven techniques adapted from cyber social-engineering research. A second coder independently reviewed coding; initial agreement was 92%, followed by discussion to reach full agreement. The study reported tactic/technique frequencies by scam type and used one-way ANOVA to compare reported losses between low- and high-complexity cases where sample sizes allowed.

          

        
                  

            
Limitations

            
The records are self-reports audited for completeness rather than factual accuracy, and reported losses were not verified. California records did not systematically include loss amounts, requiring narrative coding when available. Small and uneven scam-type samples made results exploratory and prevented parametric loss comparisons for most scam types. The sample is limited to complaints published by two state trackers.

          

        
                  

            
Future Work

            
Continue collecting complaints to enable loss comparisons for smaller scam types. Test and implement psychologically informed education, platform features that interrupt scripted interactions, standardized narrative reporting, and near-real-time pattern recognition using PTac/PTech indicators. Coordinate regulators, platforms, and law enforcement to disrupt recurring scam strategies.

          

              

    
    

        
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