Corporate Governance and FinTech Innovation: Evidence from Saudi Banks
Bibliographic record
Abstract
The rising adoption of FinTech is changing the financial sector. However, the determinants of FinTech have not been examined thoroughly. The purpose of this paper is to examine whether corporate governance is related to FinTech products in the banking sector, given that governance may influence the quantity and quality of innovation. Specifically, we investigate the association between the size of the board of directors, the percentage of independent directors on the board and FinTech services. Furthermore, we show how the composition of the board can influence the association between FinTech services and a bank’s performance. Using a sample of 12 Saudi banks for the period 2014–2019, we find that board size is significantly and negatively associated with a bank’s FinTech score. We further show that independent members on the board contribute to performance by bringing more FinTech services (innovation development) to the banks. As the first study examining the determinants of FinTech in the Saudi banking sector, this paper may help regulators to better understand the drivers of FinTech and its quality in the banking sector.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".