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A Sinister Fattening: Dissecting the Tales of Pig Butchering and Other Cryptocurrency Scams

Marilyne Ordekian ; Antonis Papasavva ; Enrico Mariconti ; Marie Vasek (2024) — 2024 APWG Symposium on Electronic Crime Research (eCrime)

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DOI: 10.1109/ecrime66200.2024.00016 (opens in a new tab)

Type:   Proceedings Article
Country:   United Kingdom
Tags:   California Dfpi, Complaint Narratives, Cryptocurrency Scams, Financial Losses, Fraudulent Trading Platforms, Pig Butchering, Platform Hopping, Social Engineering
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Synopsis

            
The researchers analyzed 143 cryptocurrency-scam incidents from 133 consumer complaint narratives in California's DFPI Crypto Scam Tracker, collected in January 2024. Three authors used concept-driven thematic analysis and a 19-theme codebook, adapting scam-lure principles after coding 20 narratives. Scammers commonly ended communication on messaging applications regardless of their initial contact channel. Scammers used up to three applications during a scam and typically redirected victims to another application after establishing trust. The tracker consists of consumer complaints summarized by the California DFPI, and the DFPI does not verify victims' reported losses.

          

        
                  

            
Identified Gaps

            
The paper identifies a need for standardized, publicly available complaint narratives across jurisdictions and for stronger mechanisms to receive and act on them. It also indicates that off-chain, fiat-linked cryptocurrency fraud is difficult to measure, and that data focused on successful complaints limits understanding of failed attempts. More evidence is needed on complex scams spanning diverse messaging platforms, their taxonomy, and offender modus operandi.

          

        
                  

            
Methods

            
The researchers analyzed 143 cryptocurrency-scam incidents from 133 consumer complaint narratives in California’s DFPI Crypto Scam Tracker, collected in January 2024. Three authors used concept-driven thematic analysis and a 19-theme codebook, adapting scam-lure principles after coding 20 narratives. They coded scam types, personas, communication platforms, and monetary losses; estimated ranges for vague loss reports through coder consensus; and applied descriptive analysis, scam-type co-occurrence analysis, Markov-chain platform-transition modeling, cumulative distributions, and two-sample KS tests.

          

        
                  

            
Limitations

            
The tracker consists of consumer complaints summarized by the California DFPI, and the DFPI does not verify victims’ reported losses. Monetary-loss data were absent in 19 cases and vague in 34 cases, requiring estimated ranges. Documentation inconsistencies required researchers to decide whether reports represented separate incidents. The dataset is inherently skewed toward successful scams, with only seven unsuccessful attempts, limiting conclusions about unsuccessful or interrupted fraud.

          

        
                  

            
Future Work

            
Develop better mechanisms for receiving and acting on scam complaint narratives. Encourage organizations worldwide to publish case narratives and standardize reporting to enable pattern identification. Use deeper narrative analysis to refine scam taxonomies, understand offenders’ modus operandi, and develop identification methods. Conduct more research on narratives because direct collection from criminal advertisements is less effective for complex, multi-platform scams.

          

              

    
    

        
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