Consumer Savings and Digital Remittance in Open Banking: Insights From Bibliometric and Geospatial Econometric Analysis
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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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.044 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".