Improving financial crime control, disruption, and prevention capabilities in the Australian Commonwealth public regulatory sector by examining intraorganisational intelligence structures
Bibliographic record
Abstract
Financial crime poses a significant threat to the integrity and accountability of the Australian Commonwealth public regulatory sector by undermining institutional legitimacy, eroding public trust, and creating systemic risks that extend beyond individual cases of misconduct. Despite the regulatory sector’s pivotal role in addressing financial crime, intelligence functions remain underdeveloped and understudied, limiting agencies’ ability to detect patterns, anticipate risks, and engage in proactive prevention rather than reactive enforcement. The research detailed in this thesis examines the internal organisational structures of intelligence capabilities within Australian Commonwealth public sector regulatory agencies and evaluates their effectiveness in supporting financial crime control, disruption, and prevention using outcome-focused metrics adapted from intelligence academics and financial crime studies. An exploratory qualitative methodology grounded in organisational theory and a social constructivist worldview was adopted to examine current intraorganisational intelligence structures, their effectiveness metrics, and comparative international practices. Data was gathered through surveys, interviews, and secondary document analysis. The findings reveal that many regulatory agencies rely on outdated organisational models, lack coherent intelligence strategies, and face systemic challenges such as limited leadership support, insufficient training, and policy constraints, which collectively constrain regulatory effectiveness. To address this these gaps, this thesis proposes a more integrated, decentralised, and collaborative intelligence structure tailored to the regulatory environment, designed to enhance intraagency coordination. Comparative insights from similar public regulatory agencies in Canada, New Zealand, and the United Kingdom further inform recommendations for reform and highlight the feasibility of adapting successful models to the Australian context. The research contributes original theoretical and practical knowledge to the fields of intelligence studies, organisational theory, and public administration, by providing a framework of enabling factors (governance and leadership, ICT, human resources, legislation and policy, and research) for enhancing intelligence-led approaches in regulatory environments. By linking intelligence outcomes to organisational effectiveness, this thesis advances understanding of intelligence as a governance function and strengthen the capacity of the Australian Commonwealth regulatory sector to prevent, detect, and disrupt financial crime more effectively
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".