Impact of Third-Party Payment on Banks’ Risk-taking Capacity and Empirical Analysis
Bibliographic record
Abstract
Third parties have provided people with a lot of convenience through the rapid development of Internet technology. In the meantime, commercial banks will face significant disruption from the characteristics of third-party payments, which include low operating costs and high settlement efficiency, as the Internet financial platform evolves. This article’s goal is to conduct an empirical analysis of how third-party payments affect commercial banks’ ability to take on risk by using imbalanced panel data from 36 listed banks between the first quarters of 2013 and the first quarter of 2021. The results show that: (1) the third-party payment significantly increases commercial banks’ ability to take on risk. (2) third-party payment increases the risk-taking capacity of non-state-owned banks compared to state-owned banks. (3) third-party payment decreases bank profitability and raises bank credit risk, which impacts commercial banks’ ability to take on more risk. Based on the above conclusions, this paper puts forward some meaningful suggestions to the relevant regulatory agencies and commercial banks.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".