Considerations for Using Artificial Intelligence to Manage Authorized Push Payment (APP) Scams
Bibliographic record
Abstract
Abstract—Artificial Intelligence (AI)-Based Security Intelligence Modelling can be used to prevent, detect, and manage cyber threats. Data-driven AI solutions are currently undergoing rigorous research and design in their own field, but few scholars or practitioners frame Authorised Push Payment (APP) Scams as a unique cybersecurity concern, or tailor technical solutions based on the local regulatory context. Drawing on a recent consultation publication by the UK Payment Systems Regulator on APP scams (November 2021), this paper 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 socio-technical 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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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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".