Ritualisation and money laundering in the Swiss banking sector
Bibliographic record
Abstract
Purpose This paper aims to highlight the relationship between money laundering and the patterns of behaviour evident throughout the larger structural environment of the Swiss banking sector. In particular, the paper used HSBC as a prototype case of structural ritualisation to show that the normalisation of corrupt, unethical behaviour in the banking environment has shaped and influenced the behaviour and actions of the embedded group actors. Design/methodology/approach The paper used a content analysis methodological approach of media sources to collect data. The content analysis was categorised into six core ritualised symbolic practices (RSP) categories – corruption, reputation, blame, ignorance, regret and criticism. Findings The findings reveal that the highly ranked RSPs involving corruption, reputation, blame, regret, ignorance and criticism influence the embedded group’s patterns of behaviour, and they formed part of the cognitive script that dictated their behaviour and actions in the Swiss banking sector. Practical implications The paper added to the calls by Swiss policymakers for amendments to Swiss bank secrecy laws to reflect the changing landscape of international banking and finance. Originality/value This is the first paper of its kind to study ritualised illegal practices related to money laundering in the Swiss banking sector.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".