Bibliographic record
Abstract
Purpose The purpose of this paper is to present an argument for the use of cognitive interviews to be use in financial crime investigations. In particular, the paper argues that the components of cognitive interview make it useful for financial crime investigators to gather and collate information on financial criminality. Design/methodology/approach The paper chronicles the literature on cognitive interviews to critically evaluate its usefulness in previous studies. Findings A critical examination of the literature shows that cognitive interviews were successfully used in a variety of circumstances. Despite its difficulties, the empirical evidence reveals that cognitive interview fared well in laboratory studies across different (and vulnerable) population groups. Practical implications There is evidence to suggest that cognitive interviews can be an effective technique to interview witnesses of financial crimes. The fact that white-collar criminals, more often than not, comes from a “gentleman background” and are not accustomed to the role of “criminal suspect,” makes cognitive interview techniques a useful tool for fraud investigators. Originality/value To the author’s knowledge, this is the first paper of its kind to conduct a thorough literature review and apply cognitive interview techniques to financial crime investigation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".