Role of Due Diligence in Combating Money Laundering in Lens of ESG: A Concept
Bibliographic record
Abstract
This study focuses on the implementation of Customer Due Diligence (CDD) requirements under Section 16 of the Anti-Money Laundering Act (AMLA) in Reporting Institutions (RIs). While RIs have established policies and procedures to comply with AMLA and mitigate risks associated with money laundering, there needs to be more emphasis on gathering information to address Environmental, Social, and Governance (ESG) Factors. ESG concerns have become increasingly important in the financial sector, but many institutions need help integrating ESG considerations into their due diligence processes. This study explores the incorporation of ESG due diligence within CDD practices, as it is crucial for long-term value creation, investor returns, and mitigating ESG risks that might be exposed to money laundering. This research aims to identify and propose practical strategies for integrating ESG factors into the customer evaluation process of RIs OR investigate factors that influence the role of due diligence in combating money laundering through the lens of ESG. This paper examines factors that influence the function of due diligence in tackling money laundering through the lens of ESG.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".