Metaverse: welcome to the new fraud marketplace
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the risks of the metaverse ecosystem. This study provides an overview of the metaverse and its evolution and discusses the various fraud risks it poses for organizations (including boards of directors, forensic accountants, auditors and accountants). Given the advantages of the metaverse and the growing interest it is attracting from organizations, this paper sheds light on the importance of mitigating its risks. Design/methodology/approach Based on a systematic review of the literature on the metaverse and analysis of the fraud triangle, this study examines the different fraud risks it poses. More specifically, this study analyzes 21 articles on the metaverse published between 2021 and 2022 and attempts to answer the following research questions: What are the risks inherent in the metaverse? What are the fraud risks associated with it? What are the opportunities and pressures it brings? What is the rationalization underlying its use? This study conducts the analysis on two levels, that of the individual (user) and that of the organization. This paper summarizes the findings of publications on the metaverse in 2021 and 2022 to discover its various definitions and the opportunities and risks it represents. Findings This paper offers an insightful discussion of the advantages and risks the metaverse can bring. Because this analysis shows that any organization could be vulnerable to metaverse risks, this study provides organizations with strategies to deter, detect and prevent fraud and reputational risks. Regulatory bodies, financial authorities, board of directors and fraud investigators should all consider these risks before investing in the metaverse. Originality/value This paper adds new insights to the scarce research on the metaverse and cybersecurity by exploring the opportunities and risks it presents. It has several implications for organizations, boards of directors, management and regulatory authorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.037 | 0.051 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".