From Fintech to Financial Stability: The Role of ESG, Basel III Liquidity Ratios, and Default Risk in European Banking
Bibliographic record
Abstract
Our study examines the relationship between fintech adoption and liquidity management in European banking, investigating how digital transformation influences Basel III liquidity compliance and default risk. Using a sample of 45 European banks from the STOXX 600 index over 2019–2024, we employ textual analysis of annual reports to construct a fintech adoption index and examine its effects on liquidity coverage ratio (LCR) and net stable funding ratio (NSFR). Our findings demonstrate that fintech adoption significantly enhances banks’ liquidity management capabilities. However, ESG performance moderates this relationship, with higher ESG commitments weakening the positive fintech-liquidity association, suggesting resource allocation conflicts between sustainability and technological investments. Through mediation analysis, we find that liquidity management partially mediates the fintech-default risk relationship, revealing complex trade-offs where fintech-driven liquidity improvements may increase default risk through alternative channels. Robustness tests using lagged variables, propensity score matching, alterative proxies, and size-based subsamples confirm our findings. Notably, smaller banks derive substantially greater liquidity benefits from fintech adoption compared to larger institutions. Our results provide the first comprehensive analysis of how digital transformation affects regulatory liquidity compliance in European markets, offering important implications for bank management and regulatory oversight in the post-Basel III era.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".