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The evolution of investment cyber scams: vulnerability and victim blaming in the cryptocurrency era

Monica Therese Whitty ; Amin Sakzad (2026) — Computers & Security

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DOI: 10.1016/j.cose.2026.104950 (opens in a new tab)

Country:   Australia
Tags:   Australia, Cryptocurrency Scams, Hybrid Romance Investment Scams, Investment Cyber Scams, Pig Butchering Scams, Self Blame, Underreporting, Victim Vulnerability
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Synopsis

            
This study investigates how investment cyber scams have evolved in the cryptocurrency era and how vulnerability to these scams is distributed across the Australian population. Using a nationally representative survey of 2,019 Australian adults, the authors examine the prevalence, shifting tactics, and predictors of investment cyber scam victimisation from 2015 to 2025, aiming to illuminate how digital access, skills, and usage relate to risk. The analysis finds that 2.6% of Australian adults reported at least one investment cyber scam experience over the decade, corresponding to roughly 550,000 people when extrapolated to the population. Victimisation rose alongside major cryptocurrency market cycles and the use of platform-embedded fraud and hybrid scams that blend investment pitches with relational tactics. Contrary to some expectations, higher cryptocurrency knowledge and greater investment experience were associated with higher risk, particularly among men. Victims often blamed themselves and cited the appearance of legitimacy as central to their deception, rather than attributing fault to criminals. Money transfers frequently flowed through cryptocurrency channels, with cryptocurrency-based methods increasingly common relative to other transfer types. The authors assess the Digital Divide theory as an explanatory lens and find limited support; vulnerabilities did not align neatly with digital access gaps. They highlight that prevention should extend beyond awareness campaigns to exposure-aware, behaviorally informed, and platform-level interventions. Limitations include self-reported data and the absence of personality or behavioral trait measures. The study underscores the need for cross-sector, data-informed strategies to detect and disrupt evolving, cryptocurrency-enabled investment scams while addressing the psychological harms experienced by victims.

          

        
                  

            
Identified Gaps

            
Investment-scam vulnerability has often been treated as a general phenomenon despite evidence that susceptibility differs by scam type. The application of Digital Divide theory to online investment scams had not been examined. The evolution and variation of investment scam forms, common deception methods, and victim blame attributions also required quantification, including hybrid romance-investment scams.

          

        
                  

            
Methods

            
A nationally representative online omnibus survey of 2,019 Australian adults was conducted by YouGov in November 2025. Participants reported scam victimization, investment experience, cryptocurrency knowledge, scam types and timing, deception and payment methods, financial losses, and attributions. Analyses used logistic and Firth penalized logistic regression, Spearman correlation, Cochran’s Q, and pairwise McNemar tests.

          

        
                  

            
Limitations

            
The nationally representative cross-sectional survey relied on self-reported victimization and is susceptible to recall bias and social-desirability effects; its 10-year recall period may introduce error. Estimates are approximate and correlational rather than causal. Perceived cryptocurrency knowledge and investment experience did not measure objective competence. The study did not include personality or behavioral characteristics that may predict vulnerability.

          

        
                  

            
Future Work

            
Replicate the findings with alternative data sources and in other national contexts. Include objective assessments of cryptocurrency knowledge alongside self-reported competence to distinguish actual skill from overconfidence. Incorporate personality and behavioral characteristics, and explore machine-learning approaches for risk profiling and vulnerability clusters.

          

              

    
    

        
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