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Record W3032292876 · doi:10.1108/jfc-12-2019-0165

Mapping the individual and structural theories of financial crimes

2020· article· en· W3032292876 on OpenAlexaff
Mark Lokanan, Indy Aujla

Bibliographic record

VenueJournal of Financial Crime · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsOriginalitySituational ethicsCommitPosition (finance)Constructive fraudMoney launderingValue (mathematics)BusinessAccountingPublic relationsCriminologyLaw and economicsSociologyPolitical scienceFinanceLawCreativity

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to argue for an integrated explanation of financial fraud. Greater emphasis must be placed on the structural and situational factors that are the elements of fraud risks and fraud. Design/methodology/approach The paper is based on a review of the literature on the explanation of financial fraud. Both micro- and macro-theoretical explanations of fraud were analysed to allow for a broader picture of the types of individuals that were involved in fraud, the rules governing their conduct and the types of law they broke. Findings The main reason why people commit fraud is that their crime propensity interacts with the elements present in criminogenic environments. Indeed, because most of the research on structural theories of fraud focuses on general criminality, not much has been done in the area of financial fraud. More research needs to be carried out to excavate the subterranean cluster of narrative on fraud risks and fraud. Research limitations/implications To address the future contingency of fraud risks, the paper adopted a similar position of prior accounting research on financial crimes. The structural explanation of fraudulent behaviour considers individuals’ actions to be less the result of individual deviance and more the cause of societal forces. Structural theories take into consideration the individual psychology of the offenders and position it to reflect the various realities – institutional, structural and cultural life – they are caught up in. Future research must endeavour to address these concerns. Originality/value The manuscript is among a new stream of literature that addresses the structural elements of financial fraud.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.283
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2020
Admission routes1
Has abstractyes

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