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COVID-19 and cyber fraud: emerging threats during the pandemic

Ma, Katelyn Wan Fei ; McKinnon, Tammy (2021) — Journal of Financial Crime

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DOI: 10.1108/jfc-01-2021-0016 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   AI-Enabled Fraud, Covid 19, Cyber Fraud, Fraud Typology, Psychology, Romance Scams, Victim Experience
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Synopsis

            
The publication examines how the COVID-19 pandemic has shaped cyber fraud by outlining how illness-related anxiety, shifts to online activity, and rapid social change create opportunities for criminal exploitation. Its purpose is to analyze COVID-19–themed cyber fraud through psychological and traditional criminological lenses and to present a pandemic‑specific typology intended to help researchers and practitioners classify and manage emerging risks. Methodologically, the article adopts a typology approach within a broader conceptual framework. It synthesizes psychological context, classic criminological theories (notably anomie and strain), and a review of fraud reports from law enforcement and industry sources to ground its discussion. Rather than presenting new primary data, the authors assemble observed incidents and agency findings to develop a preliminary categorization and to illustrate how fraud operates in the pandemic setting. The core contribution is a four‑part taxonomy of COVID‑19–themed cyber fraud: unauthorized transactions using financial information; unauthorized transactions using identity information; authorized transactions without fraudulent intent; and authorized transactions with fraudulent intent. Each category is linked to mechanisms such as domain spoofing, social engineering, government relief scams, contact tracer fraud, romance and employment scams, and virtual‑asset money laundering, with attention to the emotional appeals that criminals exploit and to reporting gaps in undercounted incidents. The article also outlines policy and practice implications, advocating multi‑stakeholder coordination, improved policing accountability, and technology‑driven defenses (e.g., AI‑assisted biometrics, stronger remote‑work security). It acknowledges limitations in the evidence base and calls for ongoing attention as the pandemic evolves.

          

        
                  

            
Identified Gaps

            
The paper identifies limited scholarship that classifies cyber fraud through end results rather than techniques. It also notes that available agency reports lack dedicated COVID-19-related virtual-asset fraud figures. More broadly, reported COVID-19 fraud totals may be incomplete because cases can be underreported or classified as regular fraud rather than pandemic-related fraud.

          

        
                  

            
Methods

            
This is a conceptual general review using a typology approach. The authors synthesize selected theories, published research, law-enforcement publications, industry reports, and observed COVID-19-related fraud incidents. Using an empirical-to-conceptual approach and deductive reasoning, they classify pandemic-themed cyber fraud into four categories based on authorization status and whether financial or identity information is compromised.

          

        
                  

            
Limitations

            
The article explicitly provides no original quantitative or qualitative data. Its taxonomy categories can overlap, and the boundary between regular cyber fraud and COVID-19-related fraud is difficult to define precisely. Reported fraud data may understate the problem because victims may not report, may not recognize victimization, or may classify COVID-19-related incidents as ordinary fraud.

          

        
                  

            
Future Work

            
Further work should examine AI-driven biometric authentication compatible with masks, gloves, and other personal protective equipment. The paper also supports developing and assessing comprehensive behavioral, IP-address, photograph, and text-based fraud-detection systems. It recommends designing a shared government framework to identify, assess, and prioritize fraud involving vulnerability, and evaluating coordinated public-private responses and victim case-tracking systems.

          

              

    
    

        
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