Financial open-source intelligence (FININT OSINT)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores the often-overlooked domain of financial intelligence (FININT) and its growing significance within the intelligence landscape, particularly through open-source financial intelligence (OSINT FININT). The paper begins by defining FININT and distinguishing it from other intelligence disciplines, with a specific focus on how financial data, traditionally confined to governmental use, is increasingly available through open sources. Key sources of open-source financial intelligence, including blockchain data and leaked financial records, are examined, demonstrating how these sources empower OSINT researchers to uncover corruption, terrorism financing, and illicit financial activity. The article also discusses the practical applications of OSINT FININT, as well as its limitations – such as issues of privacy, accuracy, and ethical concerns. Finally, the article sets forth a research agenda to address the growing complexities and ethical challenges posed by the open accessibility of financial intelligence. By mapping out the emerging landscape of FININT, this paper aims to provide a foundation for future academic inquiry into the evolving role of financial intelligence in global security and governance.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it