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Record W1579484820 · doi:10.1108/jmlc-08-2014-0025

Identifying money laundering

2015· article· en· W1579484820 on OpenAlexaboutno aff
Stephen Sterling

Bibliographic record

VenueJournal of Money Laundering Control · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMoney launderingCashContext (archaeology)BusinessValue (mathematics)AccountingEconomicsFinanceComputer science

Abstract

fetched live from OpenAlex

Purpose – This study aims to develop a newer, revised model of money laundering of general application, and to apply that updated laundering model to the use of cash in Canada. A wide-ranging analytical tool for identifying money laundering is described, which demands a comparative evaluation of available financial choices against choices made, concentrating on factors which matter most to economic enterprises: speed, cost and security. The model is applied to bulk cash money laundering and the use of cash in the Canadian context, a mature economy where cash is predominantly used for micro-payments. The inference of criminality to be drawn from bulk use of cash is explored, as is any need for continued circulation of large denomination banknotes. Design/methodology/approach – Extensive criminal investigative experience is juxtaposed with practices of legitimate commerce. As to patterns of transactional conduct, a review is undertaken of publications from financial institutions including the Bank of Canada. Findings – The model may be applied generally. In light of modern banking realities, strong inferences of criminality arise from the bulk use of cash. Research limitations/implications – Documented standards of legitimate commerce and proven laundering behaviours provide more reliable evidence than voluntary disclosures from surveys. Practical implications – The model promotes an objective analysis of financial conduct either in conjunction with, or independent of extrinsic evidence, and can augment historic lists of laundering indicators and identify new laundering typologies. Originality/value – The speed, cost and security model moves towards a renewed paradigm for understanding laundering, beyond traditional cash-based models. This instructive model applies to the full spectrum of laundering, from frauds to cash-based street crimes. By examining the inherent characteristics of financial choices, investigations may proceed without tipping off targets. The model maximizes the investigative value of know-your-customer information.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0110.006
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.071
GPT teacher head0.325
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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