Skip to main content

Old techniques, new technologies: Exploring patterns from scammers that commit financial fraud via cryptocurrency

Brandon Dulisse ; Chivon Fitch ; Jaden Denis ; Nathan Connealy (2025) — Journal of Economic Criminology

Citation activity

Total citations · Google Scholar
Unavailable
Average per calendar year
Citation count unavailable

Counts reflect Google Scholar’s coverage and do not measure research quality. Source, calculation and limitations

View Original Source (opens in a new tab)

DOI: 10.1016/j.jeconc.2025.100178 (opens in a new tab)

Type:   Journal Article
Country:   United States
Tags:   Cryptocurrency Fraud, Financial Loss, Fraudulent Trading Platforms, Pig Butchering, Repeat Victimization, Romance Scams, Scam Trackers, Social Media Scams
What do these research terms mean?
Preprint
A manuscript shared before formal peer review and publication. Check whether a later published version is available.
Dataset
A collection of data or examples for others to inspect or reuse. It can appear in Library search and topic mapping, but RSRC does not use dataset records as evidence in Research Insights.
Dissertation or thesis
Research submitted for an academic degree. This describes its format, not its reliability.
Journal article
An article published in a journal. This label alone does not establish peer review, study quality, or how well the findings apply elsewhere.
Qualitative research
Examines experiences, meanings, or processes, often through interviews or observations. It can explain how something happens without estimating how common it is.
Quantitative research
Uses numerical measurements to describe patterns or test relationships. A relationship between two measurements does not by itself show that one causes the other.
Systematic review
Uses a planned, documented method to find and assess research addressing a question. Its conclusions still depend on the included studies and what the search covered.
Meta-analysis
Statistically combines results from multiple studies. Combining studies does not remove weaknesses in their design or make unlike populations interchangeable.
Not classified
This record has no recognized label in this filter. It does not mean the publication used no method, or that no research exists.

Definitions draw on DataCite resource types; Cochrane review methods; NLM: association and causation. RSRC’s dataset and classification rules are explained in our methodology.

Citation tools
Download RIS Download BibTeX 


              
              

            

          

        


      

      

    

        
Transparency

        
Evidence and review status

        
This page contains AI-generated content. No human content review or subject-matter-expert review is recorded.

    


    

                    

                
Source basis

                

                    Downloaded PDF
                

            

                    

                
Source updates

                

                    No notice found at last check
                

            

                    

                
AI-generated page content

                

                    Yes
                

            

                    

                
Automated checks

                

                    Passed
                

            

                    

                
Administrative approval

                

                    Yes
                

            

                    

                
Human content review

                

                    Not recorded
                

            

                    

                
Subject-matter-expert review

                

                    Not recorded
                

            

            


    

        
            How this was prepared
        
        

                            

                    
Source basis

                    

                    
RSRC downloaded and privately stored a copy of the paper for internal analysis. The PDF is not offered to viewers from this page.


                                            
    
                                                            
  • 
                                    PDF added to RSRC:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
Source updates

                    

                    
No incoming update notice was found in the dated Crossref response. Coverage is incomplete, particularly for corrections and expressions of concern; this is not a guarantee that the source is valid or unchanged.


                                            
    
                                                            
  • 
                                    Last source-status attempt:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
AI-generated page content

                    

                    
AI-generated research notes displayed on this page: Synopsis, Identified gaps, Methods, Limitations, Future work. The paper itself is not described as AI-generated.


                                            
    
                                                            
  • 
                                    Document analysis recorded:
                                    
                                
    • 
                                                            
  • 
                                    Synopsis generation recorded:
                                    
                                
    • 
                                                            
  • 
                                    Page record updated:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
Automated checks

                    

                    
The current, source-bound synopsis passed the recorded versioned publication checks.


                                            
    
                                                            
  • 
                                    Checks completed:
                                    
                                
    • 
                                                    

                    
                                            

                            
                                View passed checks (3)
                            
                            
    
                                                                    
  • Length, completeness, repetition, refusal, boilerplate, and active-markup screening
    • 
                                                                    
  • Numerical claims checked against the available source text
    • 
                                                                    
  • English-source lexical grounding check
    • 
                                                            

                        

                                        

                

                            

                    
Administrative approval

                    

                    
An authenticated administrator approved the bibliographic record for public Library display. This is not a review of every research claim.


                                            
    
                                                            
  • 
                                    Approved for public display:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
Human content review

                    

                    
No human review is recorded for the AI-generated content displayed on this page.


                    
                                        

                

                            

                    
Subject-matter-expert review

                    

                    
RSRC has not recorded review of this content by a subject-matter or methods expert.


                    
                                        

                

                    

    


    

        Review-state definitions

        Found a possible error?
        Request a correction.
    



    



              

        
                  

            
Synopsis

            
This publication investigates cryptocurrency-related scams by analyzing complaint data from two state scam trackers—California’s DFPI and Wisconsin’s DFI—launched in 2023 and 2024, respectively. The study treats crypto fraud as a convergence of traditional online deception and new digital platforms, aiming to describe how scammers contact victims, identify what scam types are most prevalent and costly, and assess whether higher victimization frequency relates to greater losses. The authors code narrative complaint descriptions to extract patterns across social media channels, messaging apps, and other contact points, and to categorize scam “titles” used to establish legitimacy. Key findings indicate that two scam types dominate crypto fraud: fraudulent trading platforms and pig butcher­ing scams, which together account for a substantial share of cases. Victims are primarily reached through online messaging apps and social media, with titles such as authority figures (professors, coaches, mentors) and financial professionals used to gain trust; family or known acquaintances also feature prominently in some schemes. The analysis shows that multiple victimizations tend to be associated with larger total losses, and that pig‑butchering scams, in particular, yield the highest average losses compared with several other scam families. The study acknowledges several limitations: the data come from two states and rely on self‑reported consumer complaints that are not systematically verified for accuracy; incomplete narratives and missing data constrain certain analyses; and results are exploratory rather than generalizable. Despite these caveats, the work contributes descriptive insight into how social media, cryptotrading platforms, and romantic or familiar tropes facilitate crypto fraud, with implications for public education, consumer awareness, and platform‑level prevention efforts.

          

        
                  

            
Identified Gaps

            
The study identifies limited knowledge about the initial point of contact between cryptocurrency-fraud victims and offenders. It also notes gaps in scam-tracker data, including structured details on offender introductions, communication frequency and style, contact initiation, trading platforms, number of requests or victimizations, and total losses. The authors call for more consistent and robust information on victims, offenders, and scams across jurisdictions.

          

        
                  

            
Methods

            
The study conducted exploratory descriptive and regression analyses of 291 self-reported consumer complaints from California's cryptocurrency scam tracker (n=260) and Wisconsin's investment scam tracker (n=31). Researchers coded complaint narratives for contact channel, implied victim gender, offender titles, scam type, reported losses, and single versus multiple victimizations. They compared loss amounts across scam types and offender-title categories and used regression to test whether repeat victimization predicted total loss.

          

        
                  

            
Limitations

            
The sample is limited to publicly accessible trackers in California and Wisconsin, so complaints may not represent all cryptocurrency fraud. Reports could potentially originate outside those states. Tracker complaints were checked for completeness but not accuracy, and the authors lacked control over secondary-data measures. Some narratives were vague, making scam classification difficult. Missing data were notable for implied victim gender and loss amounts; findings are exploratory and limited to victim-provided information.

          

        
                  

            
Future Work

            
Future research should examine how scammers find targets and use communication techniques, romance, financial success, blackmail, or desperation to deceive them. Studies could integrate additional data, conduct detailed case studies of victim disclosures, and examine points of monetary transfer. Researchers should also assess how technologies such as social media, messaging, cryptocurrency, and trading platforms create advantages for offenders and make legitimate opportunities difficult to distinguish from fraud.

          

              

    
    

        
Related Research Insights

        

            See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.