The Progress of Fintech and the Vulnerability of the Banking System: An Empirical Test from China
Bibliographic record
Abstract
The vulnerability of the banking system constitutes a critical concern within the broader financial system. Given the high degree of interconnection among financial institutions, banks are particularly susceptible to systemic risk. The eruption of financial crises frequently exerts far-reaching effects on economic and market stability. This study employs panel data from Chinese listed banks spanning the period 2007 to 2022 to investigate the relationship between fintech development and the systemic vulnerability of the commercial banking sector. Empirical findings indicate that fintech development tends to intensify systemic risks, particularly in banks characterized by weak regulatory oversight or limited profitability. Furthermore, financial management practices and internal governance mechanisms demonstrate a significant moderate effect in the relationship between fintech development and systemic risk. The analysis also reveals that different types of banks and credit structures exhibit heterogeneous responses to fintech innovations. These findings underscore the importance of implementing differentiated regulatory approaches tailored to the specific characteristics of individual banks. By integrating the influence of fintech into the existing systemic risk framework, this study addresses a gap in the current literature, offering a theoretical foundation for both banking institutions and financial regulators to develop effective risk mitigation and supervisory strategies in the context of rapid fintech advancement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".