From cash to contactless: Structural break evidence from Saudi Arabia’s POS transaction patterns
Bibliographic record
Abstract
Point-of-sale (POS) transactions are considered one of the fundamental components of retail financial systems. The performance of the POS service network relies heavily on a reliable and secure e-banking infrastructure driven by usage patterns. In this study, we perform a structural break analysis to identify all points in the POS transaction time series where major structural behavioral shifts have occurred. We then investigated the potential sources of these shifts in the data. The analysis revealed four major shifts between 2019 and 2023. The corresponding events, such as the introduction of Apple Pay, the broader rollout of MADA’s contactless and mobile payment infrastructure, COVID-19, SAMA’s regulatory sandbox and open banking pilot projects, and structural changes in banking technology and regulation, are evident as potential drivers of these changes. These results are useful for planners doing digital infrastructure management, expansion, and investment planning, as well as performing policy formulation and responses.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".