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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 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".