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Victim Profiling Under Financial Crimes

Pankaj Bhardwaj ; Chander Mohan Gupta (2025) — Fundamentals of Forensic Victimology: Insights into Crime, Trauma, and Justice, pp.33–38

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DOI: 10.1007/978-3-031-95987-5_3 (opens in a new tab)

Country:   Global
Tags:   Digital Literacy, Financial Crime, Fraud Vulnerability, Online Fraud, Prevention, Romance Scams, Social Engineering, Victim Profiling
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Synopsis

            
Victim Profiling Under Financial Crimes examines how fraudsters identify and exploit individuals who are most susceptible to financial deception. The article frames victim profiling as a deliberate analysis of psychological, social, and behavioral characteristics that can make someone vulnerable to schemes such as phishing, romance scams, investment fraud, and other cyber-enabled or traditional financial frauds. It notes that rapid digitalization and cross-border crime complicate victim identification and protection, and it emphasizes that understanding victim vulnerabilities can inform regulatory design and preventive strategies. The authors describe a multifactor basis for profiling, including psychological biases (such as impulsivity and overconfidence), demographic factors (age, income, education), technological familiarity, and emotional or social conditions (distress, loneliness). They summarize methods fraudsters use to locate victims, such as social media analysis, data from breaches, and AI-assisted phishing, and they discuss how criminals leverage social engineering and psychological manipulation to induce risky financial behavior. The article also catalogs common fraud types where profiling plays a role, including phishing, romance scams, and business email compromise, and highlights the alleged need for financial literacy, cybersecurity awareness, and stronger consumer protections as preventive responses. The conclusion asserts that financial fraud is influenced by a combination of personal vulnerabilities and broader structural factors, and it calls for ongoing research, education, and coordinated regulatory efforts to reduce victimization. It underscores the potential value of profiling-informed prevention while noting challenges in cross-jurisdictional enforcement and the evolving digital threat landscape.

          

        
                  

            
Identified Gaps

            
The chapter identifies a need for predictive models to enable early fraud identification and intervention. It offers broad vulnerability factors but does not present a validated profiling model, original victim dataset, or a focused empirical analysis of romance-scam victims. Romance fraud is treated as one financial-crime subtype within a wider discussion.

          

        
                  

            
Methods

            
This is a conceptual, narrative synthesis of cited research on victim profiling in financial crime. It describes psychological, demographic, technological, socioeconomic, emotional, and behavioral factors associated with fraud vulnerability, and outlines offender targeting methods and prevention implications. No original data collection, sample, analytic procedure, or systematic-review protocol is reported.

          

        
                  

            
Limitations

            
The chapter does not report an original sample, data source, search strategy, study-selection criteria, or formal analytic method. Consequently, the strength and comparability of the cited evidence cannot be assessed from the text. Its treatment of romance scams is brief and embedded in a broad financial-crime overview rather than supported by dedicated romance-fraud data.

          

        
                  

            
Future Work

            
Continue research on victim profiles to develop prediction models for early fraud identification and intervention. Research should support multidisciplinary prevention that addresses human and institutional weaknesses, including financial education, digital literacy, cybersecurity practices, regulation, and coordination among banks and law enforcement.

          

              

    
    

        
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