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Industrialized heartbreak: how generative AI enables romance fraud at scale

Lorena Dominguez Castillo (2026) — AI and Ethics

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DOI: 10.1007/s43681-026-01129-9 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   AI Governance, Cryptocurrency Laundering, Deepfake, Deepfakes, Fraud Detection, Generative AI, Online Safety, Platform Accountability, Romance Scams, Sextortion, Technology Facilitated Abuse
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Synopsis

            
This correspondence synthesizes empirical findings (e.g., Gressel et al.'s large-scale experiment showing 46% compliance for LLM-powered scam agents vs 18% for humans) with real-world loss data (FBI/FTC figures) and technology-trade observations on deepfakes, voice cloning, and AI-generated imagery. It identifies two converging operational models (West African Yahoo Boys and Southeast Asian compounds) and describes how LLMs preserve narrative consistency, adapt tone, and sustain dozens to hundreds of simultaneous deceptions. Fraud victims are reported to experience psychological harm comparable to violent-crime victims, including depression, anxiety, and sometimes suicidal ideation. The paper reports that romance-fraud victims are not distinguished by low intelligence or poor judgment, but may score highly on romantic idealism and addictive tendencies. The analysis relies on secondary sources and incident reports rather than primary victim data; figures reflect reported losses and may undercount true harm.

          

        
                  

            
Identified Gaps

            
The article identifies gaps in AI ethics frameworks and platform governance that neglect interpersonal AI-enabled exploitation. It notes that harms within relationships are often treated as private misfortune and not prioritized in regulation, while safety testing targets high-salience harms. It argues for a formal harm taxonomy including romance fraud and AI-facilitated abuse, enhanced fraud-training content, and robust detection of AI-generated content on platforms. Governance failures are attributed to divided responsibility among AI developers, social-media platforms, and dating services, weak accountability, under-reporting, and a lack of political constituencies to advocate for victims. An attention gap persists due to demographic targeting and stigma around reporting.

          

        
                  

            
Methods

            
This correspondence synthesizes empirical findings (e.g., Gressel et al.’s large-scale experiment showing 46% compliance for LLM-powered scam agents vs 18% for humans) with real-world loss data (FBI/FTC figures) and technology-trade observations on deepfakes, voice cloning, and AI-generated imagery. It identifies two converging operational models (West African Yahoo Boys and Southeast Asian compounds) and describes how LLMs preserve narrative consistency, adapt tone, and sustain dozens to hundreds of simultaneous deceptions. It further analyzes platform governance failures, safety-filter gaps, and the proliferation of open training materials, arguing for intervention through governance reforms, fraud-content controls, and enhanced detection across social and dating platforms.

          

        
                  

            
Limitations

            
The analysis relies on secondary sources and incident reports rather than primary victim data; figures reflect reported losses and may undercount true harm. It synthesizes studies (e.g., one major experiment on LLM-based romance baiting) that may not generalize across cultures or languages. The article acknowledges reporting biases, under-reporting by victims, and the challenge of measuring psychological harm and platform harms that are private or diffuse. It also notes safety-filter testing is incomplete, with limited visibility into open training materials and real-world abuse patterns.

          

        
                  

            
Future Work

            
Develop formal harm taxonomy for AI-enabled interpersonal abuse; study efficacy of platform-level interventions; design and evaluate fraud-detection and AI-generated-content moderation across social and dating platforms; align with EU AI Act risk-based regulation; build victim-support and reporting mechanisms; investigate cross-border enforcement and crypto-laundering links; monitor adoption of defense measures and changes in attacker tactics; foster victim-centered governance with stakeholder coalitions.

          

              

    
    

        
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