Bibliographic record
Abstract
The chapter provides a viewpoint on the rise in artificial intelligence (AI)--based deepfake financial crimes with reference to the cases reported around the world. This chapter gives an overview of deepfake technology-based financial crimes. To investigate the instances of financial crimes perpetrated through deepfake audio, video and social media in the UK, Hong Kong, Canada, China, and India, this chapter mainly depends on secondary data sources, such as research papers, news articles, magazine articles, and reports. This chapter also discusses five media-reported cases of deepfake financial crimes. Deepfake technology threatens organizations and individuals regardless of demographics. Further, emphasis is given to the cognitive dissonance of the individual victims. Deepfakes are rising due to the increasing social media footprints. Deter the occurrence of deepfake financial crime by introducing stringent regulations and following the “double-check strategy” and “zero-trust approach.” In addition, provide proper training and awareness and get updated with recent advances in financial crimes to avoid the misuse of individual vulnerabilities and falling into “infopocalypse.” Furthermore, the perpetrators used technology to defraud people. It will also threaten banks and financial institutions, so regulations and laws must be updated to do digital due diligence. Organizations must make use of the integration of blockchain and AI to mitigate deepfake financial crime.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".