Skip to main content

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

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

Citation activity

Total citations · Google Scholar
Unavailable
Average per calendar year
Citation count unavailable

Counts reflect Google Scholar’s coverage and do not measure research quality. Source, calculation and limitations

View Original Source (opens in a new tab)

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
What do these research terms mean?
Preprint
A manuscript shared before formal peer review and publication. Check whether a later published version is available.
Dataset
A collection of data or examples for others to inspect or reuse. It can appear in Library search and topic mapping, but RSRC does not use dataset records as evidence in Research Insights.
Dissertation or thesis
Research submitted for an academic degree. This describes its format, not its reliability.
Journal article
An article published in a journal. This label alone does not establish peer review, study quality, or how well the findings apply elsewhere.
Qualitative research
Examines experiences, meanings, or processes, often through interviews or observations. It can explain how something happens without estimating how common it is.
Quantitative research
Uses numerical measurements to describe patterns or test relationships. A relationship between two measurements does not by itself show that one causes the other.
Systematic review
Uses a planned, documented method to find and assess research addressing a question. Its conclusions still depend on the included studies and what the search covered.
Meta-analysis
Statistically combines results from multiple studies. Combining studies does not remove weaknesses in their design or make unlike populations interchangeable.
Not classified
This record has no recognized label in this filter. It does not mean the publication used no method, or that no research exists.

Definitions draw on DataCite resource types; Cochrane review methods; NLM: association and causation. RSRC’s dataset and classification rules are explained in our methodology.

Citation tools
Download RIS Download BibTeX 


              
              

            

          

        


      

      

    

        
Transparency

        
Evidence and review status

        
This page contains AI-generated content. No human content review or subject-matter-expert review is recorded.

    


    

                    

                
Source basis

                

                    Downloaded PDF
                

            

                    

                
Source updates

                

                    No notice found at last check
                

            

                    

                
AI-generated page content

                

                    Yes
                

            

                    

                
Automated checks

                

                    Passed
                

            

                    

                
Administrative approval

                

                    Yes
                

            

                    

                
Human content review

                

                    Not recorded
                

            

                    

                
Subject-matter-expert review

                

                    Not recorded
                

            

            


    

        
            How this was prepared
        
        

                            

                    
Source basis

                    

                    
RSRC downloaded and privately stored a copy of the paper for internal analysis. The PDF is not offered to viewers from this page.


                                            
    
                                                            
  • 
                                    PDF added to RSRC:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
Source updates

                    

                    
No incoming update notice was found in the dated Crossref response. Coverage is incomplete, particularly for corrections and expressions of concern; this is not a guarantee that the source is valid or unchanged.


                                            
    
                                                            
  • 
                                    Last source-status attempt:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
AI-generated page content

                    

                    
AI-generated research notes displayed on this page: Synopsis, Identified gaps, Methods, Limitations, Future work. The paper itself is not described as AI-generated.


                                            
    
                                                            
  • 
                                    Document analysis recorded:
                                    
                                
    • 
                                                            
  • 
                                    Synopsis generation recorded:
                                    
                                
    • 
                                                            
  • 
                                    Page record updated:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
Automated checks

                    

                    
The current, source-bound synopsis passed the recorded versioned publication checks.


                                            
    
                                                            
  • 
                                    Checks completed:
                                    
                                
    • 
                                                    

                    
                                            

                            
                                View passed checks (3)
                            
                            
    
                                                                    
  • Length, completeness, repetition, refusal, boilerplate, and active-markup screening
    • 
                                                                    
  • Numerical claims checked against the available source text
    • 
                                                                    
  • English-source lexical grounding check
    • 
                                                            

                        

                                        

                

                            

                    
Administrative approval

                    

                    
An authenticated administrator approved the bibliographic record for public Library display. This is not a review of every research claim.


                                            
    
                                                            
  • 
                                    Approved for public display:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
Human content review

                    

                    
No human review is recorded for the AI-generated content displayed on this page.


                    
                                        

                

                            

                    
Subject-matter-expert review

                    

                    
RSRC has not recorded review of this content by a subject-matter or methods expert.


                    
                                        

                

                    

    


    

        Review-state definitions

        Found a possible error?
        Request a correction.
    



    



              

        
                  

            
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.

          

              

    
    

        
Related Research Insights

        

            See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.