Consumer Savings and Digital Remittance in Open Banking: Insights From Bibliometric and Geospatial Econometric Analysis
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
Open banking (OB) refers to financial institutions opening their data and services to external parties via application programming interfaces (APIs), a practice that has been increasingly adopted globally since its 2018 regulatory inception in the United Kingdom. Despite its growth, there is still a lack of academic studies examining its impact on consumer financial behaviors on a global scale. This study addresses this gap by exploring OB’s influence on consumers’ formal saving and digital remittance behaviors worldwide. Using a mixed methods design, we combine bibliometric analysis and geospatial econometric modeling on Scopus OB bibliographic data and consumer financial preferences data from 2021 to 2022 across 139 countries. While the bibliometric results highlight the need for more international collaborations in OB research that reflect the ongoing collaborations in its implementation around the world, the econometric findings reveal significantly positive benefits for consumers globally, increasing the likelihood of formal saving and digital remittance. Specifically, consumers in countries with Revised Payment Services Directive (PSD2)–regulated initiatives, market‐driven initiatives, and other non‐PSD2 initiatives show higher marginal utilities (MUs) from digital remittance (39.1%–56.7%) compared to those in countries without OB initiatives. Additionally, consumers in PSD2 and market‐driven countries exhibit higher MUs from formal saving by 61.8% and 37%, respectively, compared to those without OB initiatives. Overall, in addition to the implications for global open innovation, the paper provides reasonable evidence, supporting OB implementation to achieve several Sustainable Development Goals (SDGs) and the associated benefits to consumers’ worldwide.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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