Modern Offense Typologies to Reduce the Risk of Money Laundering and Increase Financial Stability and Sustainability in the United States Banking System
Bibliographic record
Abstract
This qualitative descriptive case study aimed to identify predicate offense typologies that U.S. banking and financial services company compliance managers use to reduce the risks of money laundering and sustainable financing activities. The target population consisted of 15 research participants. The data were collected using semi structured interviews, semi structured observations, and document reviews from business and finance academic journals. The data were analyzed using a coding approach, thematic analysis, and content analysis. The quintessence of this study was influenced by the participants’ lived experiences and expertise. The study’s findings uncovered predicate offense typologies related to financial risks that are increasing the risks of money laundering and sustainable financing activities. The study results indicate the benefits of modifying money laundering and sustainable financing risk mitigation approaches and developing new mitigating controls. Additionally, the study findings increase insight for compliance managers to implement strategic changes that will stimulate long-term sustainable growth and economic value. Compliance managers may understand new risks and modify operational measures to mitigate financial risk risks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".