Skip to main content

(Mis)Understanding the Impact of Online Fraud: Implications for Victim Assistance Schemes

Cassandra Cross (2018) — Victims & Offenders

Citation activity

Total citations · Google Scholar
54
Average per calendar year
6.0

Count checked .

54 citations ÷ 9 calendar years (2018–2026). The first and last years may be partial. This is a lifetime average, not a year-by-year citation history.

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

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


              
              

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 article by Cassandra Cross examines how online fraud causes harms that extend well beyond financial loss and argues for a reconsideration of who is eligible for victim assistance schemes in Australia. Using online fraud as a case study, Cross draws on narratives from 80 victims who lost at least AU$10,000, gathered through semi-structured interviews as part of a broader project. The study shows that victims often experience declines in physical and mental health, relationship breakdowns, unemployment, and other long‑term consequences, including suicidal ideation in extreme cases. The author emphasizes that current victim assistance programs typically distinguish eligibility by whether the crime was violent, excluding many online fraud victims from support. The article also highlights the complexity of online fraud victimization, noting that many victims have histories of prior victimization and that fraud can interact with ongoing abuse and trauma. In particular, the research documents cases where romance fraud victims faced abusive dynamics, manipulation, verbal aggression, and even blackmail, illustrating how online fraud can compound previous harms and create new vulnerabilities. Cross argues that these patterns of harm indicate the need for access to financial and supportive services that address overall well‑being, rather than limiting eligibility to victims of violent offenses. In terms of implications, the paper advocates shifting eligibility criteria away from an arbitrary violent/nonviolent distinction toward a harms‑based understanding of crime impact. This would potentially broaden access to counseling, medical care, and financial assistance for online fraud victims and their families. The author also discusses practical considerations for implementation and acknowledges limitations related to reporting practices and jurisdictional variation in Australia. The conclusion calls for policy reform to recognize online fraud victims as legitimate recipients of victim support.

Identified Gaps

The paper identifies no prior critical examination of whether the changing nature and severe impacts of online fraud require victim-assistance schemes to move beyond a violence-based eligibility criterion. It also identifies limited research on online fraud types and calls for more examination of links between prior domestic or sexual violence and romance-fraud vulnerability.

Methods

The article analyzes semistructured qualitative interviews with 80 Australian online-fraud victims who each lost at least AU$10,000. Participants were recruited through ACCC Scamwatch reports from five major cities. Interviews were transcribed or documented in detailed notes and coded in NVivo 11 using both axial and open coding, combining deductive and inductive analysis. Approximately one third of the sample experienced romance fraud, one third investment fraud, and one third other or mixed schemes.

Limitations

The sample was limited to reported Scamwatch victims with losses of at least AU$10,000, concentrated in five large cities, and had a 5% participation response rate. The author does not claim it represents all online-fraud victims. Some interviews were conducted by telephone, and some could not be recorded, requiring handwritten notes. Findings therefore primarily illuminate high-loss, reporting victims rather than prevalence or causal relationships in the broader victim population.

Future Work

Further research is needed to examine the relationship between prior domestic violence and sexual assault and online fraud victimization, particularly romance fraud. The paper also indicates a need to consider how victim-participation provisions in assistance legislation should be interpreted for online fraud victims so that self-blame and victim-blaming do not obstruct access to support.

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