Fintech and Financial Risks of Systemically Important Commercial Banks in China: An Inverted U-Shaped Relationship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The past decade has seen impressive developments in financial technology (FinTech) in China. As a new technology and innovative method that competes with, and also supplements, traditional financial methods, fintech has had a significant impact on traditional financial businesses and has thus challenged the role of commercial banks as credit intermediaries in the financial sector. This paper examines the potential risks that fintech brings to commercial banks in China, and collects data from 19 systemically important banks from 2011–2020 to analyze the effect of fintech development on commercial banks’ financial risks in order to achieve sustainable development in the financial sector. Using the Z value and non-performing loan ratio as the criterion variables, this study shows that the impact of fintech on the financial risks of systemically important banks demonstrates an inverted U-shaped pattern, with the financial risk increasing first and then decreasing alongside the further development of fintech. The results also show that commercial banks’ responses to fintech development has been comparatively slow. Managerial suggestions are then discussed on risk supervision for commercial banks and the financial sector in China and other emerging markets.
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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 | 0.004 |
| 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 | 0.001 |
| 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 it