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Crypto-Cognitive Exploitation: Integrating Cognitive, Social, and Technological perspectives on cryptocurrency fraud

Perdana, Arif ; Jhee Jiow, Hee (2024) — Telematics and Informatics

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DOI: 10.1016/j.tele.2024.102191 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   AI Enabled Analysis, Cognitive Vulnerability, Cryptocurrency Fraud, Defi Scams, Dfpi Reports, Fraudulent Trading Platforms, LLM Assisted Coding, Pig Butchering, Pig Butchering Scams, Prevention, Regulatory Strategies, Romance Scams, Social Engineering, Text Analytics
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Synopsis

            
This study applied cognitive-vulnerability and social-engineering theories to 204 cryptocurrency-scam reports from California's Department of Financial Protection and Innovation. Advanced language-model coding achieved an interrater kappa of 0.78, identified seven scam dimensions, and informed the proposed Crypto-Cognitive Exploitation Model. Fraudulent trading-platform schemes appeared in more than 80% of reports, while pig-butchering appeared in more than 50% and frequently co-occurred with platform fraud. Reported consequences included lost savings, debt, betrayal, shame, and diminished trust; offenders also invoked AI or automated trading to promise returns. A single U.S. reporting source, reporting and narrative bias, automated-coding error, API constraints, and no longitudinal validation limit the model's external validity.

          

        
                  

            
Identified Gaps

            
Explicit gaps include reliance on 204 DFPI reports and Chainalysis API limitations; absence of multi-source data; limited generalizability to non-US contexts; lack of longitudinal data; need empirical validation of CCEM; need to translate findings into cross-jurisdictional prevention and policy evaluations.

          

        
                  

            
Methods

            
An integrated theoretical framework combining Cognitive Vulnerability Theory and the Social Engineering Approach analyzes 204 DFPI cryptocurrency scam reports to map scam dimensions. The study uses advanced language models for data coding, achieving interrater reliability (kappa = 0.78) and introduces the Crypto-Cognitive Exploitation Model (CCEM). It identifies seven scam dimensions and notes prevalent fraudulent trading platform scams often co-occur with pig butchering.

          

        
                  

            
Limitations

            
Limitations include reliance on a single US-based data source (DFPI) and Chainalysis API restrictions, potential reporting bias, and narrative variability; limited generalizability to global contexts; no longitudinal tracking; potential measurement error from automated coding; need external validation of CCEM.

          

        
                  

            
Future Work

            
Future research should broaden data sources beyond US DFPI reports to capture global scam typologies and cross-jurisdictional responses. Validate and extend the Crypto-Cognitive Exploitation Model (CCEM) using multi-source datasets (global Chainalysis data, consumer complaint portals) and longitudinal data to track scam evolution. Empirically test the effectiveness of recommended digital strategies and regulatory countermeasures across different regulatory regimes. Investigate victim demographics and psychosocial factors to tailor prevention messaging. Develop standardized coding schemes for scam reports to improve comparability. Explore real-time detection tools and interventions in DeFi and exchange ecosystems, and assess policy impact on scam incidence and recovery outcomes.

          

              

    
    

        
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