Anti-money laundering and counter-terrorist financing threats posed by mobile money
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the various characteristics of mobile money transactions and the threats they present to anti-money laundering (AML) and counter terrorist financing regimes. Design/methodology/approach A thorough literature review was conducted on mobile money transactions and the associated money-laundering and terrorist financing threats. Four key themes were identified in relations to the three stages of money laundering and effective law enforcement. Findings The findings indicate that as money laundering and terrorist financing transactions continue to gravitate towards the weaknesses in the financial system, mobile money provides yet another avenue for criminals to exploit. Risk factors associated with anonymity, elusiveness, rapidity and lack of oversights were all integral considerations in building an effective AML regime. The use of cash is considered a higher threat than mobile money prior to implementation of systems and controls. Practical implications This rapidly changing environment of how individuals manage their money during transactions is set to further explode globally, which poses new problems for regulators and governments alike. Unless there is a unified concentration to heighten global awareness, the imposing threat of mobile money is set to increase at a rapid rate if appropriate actions are not taken. Originality/value The findings from this study can be used to gain greater insights on mobile money transactions and raise further awareness of the ever-increasing threat to global financial integrity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| 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".