The Impact of Financial Technology (FinTech) on Bank Risk-Taking and Profitability in Small Developing Island States: A Study of Fiji
Bibliographic record
Abstract
With the increasing adoption of technologies such as mobile banking and blockchain, the banking sector in developing and emerging economies is experiencing both opportunities and challenges. This study examines the impact of FinTech on bank risk-taking and profitability in the small island economy of Fiji, spanning the period from 2000 to 2024. We employ a fixed-effects model and conduct robustness checks using random effects, pooled ordinary least squares (OLS), and the generalized method of moments (GMM) method, focusing on seven banks (five commercial banks and two non-bank financial institutions). Our analysis evaluates the effect of FinTech while controlling for other bank-specific factors that may influence risk-taking and profitability. The results indicate that FinTech development significantly reduces bank risk-taking and enhances profitability, suggesting a positive and substantial impact on financial performance and stability. The findings highlight the need for banks operating in Fiji and similar small economies to continue and expand their investments in FinTech innovations. Furthermore, the study suggests that regulatory bodies and policymakers should strengthen institutional and regulatory frameworks to support and guide FinTech’s evolution within the banking sector.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".