Considerations for Using Artificial Intelligence to Manage Authorized Push Payment (APP) Scams
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
Artificial Intelligence (AI)-based security intelligence modeling can be used to prevent, detect, and manage cyber threats. Data-driven AI solutions are currently undergoing rigorous research and design changes in their own field, but few scholars or practitioners frame authorized push payment (APP) scams as a unique cybersecurity concern, or tailor technical solutions based on local regulatory contexts. Drawing on a recent consultation publication by the UK Payment Systems Regulator on APP scams (November 2021), this article shows how AI can be leveraged to manage APP scams and explores some of the opportunities and risks one should consider when adopting such an approach. We highlight three scenarios: 1) Liability on payment service provider; 2) Liability on payor; and 3) Liability on payor with substantial public sector involvement. These examples illustrate how sociotechnical systems can play a design role, and consequently assist industry leaders and engineering management in prioritizing investment focus, strategic approaches, and technical solutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it