Integrating criminological theories in accounting and finance fraud research: A systematic literature review
Bibliographic record
Abstract
Fraud in accounting and finance poses a persistent threat to organizational integrity and economic stability, necessitating robust theoretical frameworks for effective detection and prevention. While traditional accounting literature predominantly addresses fraud through objective and quantitative lenses, this study argues for the integration of criminological theories to enhance the understanding of fraudulent behaviors. This systematic literature review examines the application of micro, macro, and integrated criminological theories within accounting and finance fraud research, synthesizing insights from 14 peer-reviewed studies using the Australian Business Deans Council (ABDC) list for filtering identified through a comprehensive search of the Scopus database. The review highlights the contributions of micro-level theories, such as Merton’s Strain Theory and Sutherland’s Differential Association Theory, in explaining individual motivations for fraud. It also explores macro-level frameworks, including Institutional Theory and Ecological Theory, which provide a broader perspective on societal and organizational influences. Integrated theories, such as Situational Action Theory and Routine Activity Theory, offer a holistic approach by linking individual predispositions with contextual factors. The findings reveal a critical gap between theoretical constructs and their practical application in accounting practices, particularly in audit procedures, compliance systems, and internal controls. This study calls for future research to bridge this gap by developing empirically validated frameworks that translate criminological insights into actionable anti-fraud strategies within corporate environments. By advancing interdisciplinary approaches, this research contributes to both academic discourse and practical methodologies for enhancing fraud risk management in accounting and finance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.048 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".