Alternative Strategies of Credit Risk Management: A Successful Case Study of the Guangdong Nanyue Bank in China From 2011 to 2016
Bibliographic record
Abstract
Guangdong Nanyue Bank (GNB) shows the alternative strategies of credit risk management which led to its growth. It was formed out of local government finances and enterprise shares but spread its base to six cities and ranked as one of the top ten banks in the country. Since its establishment, the bank has been adhering to its market positioning: serving small and medium-sized enterprises, serving local citizens and serving trade financing. In order to better regulate the credit approval procedures and improve the credit level of decision-making, GNB has developed a set of applicable measures for the management of credit risk, set up corresponding departments and allocated professional staff for credit risk control before approval of loan, during the loan, and after the loan. The paper looks at the alternative strategies followed by GNB to manage credit risk and grow successfully within the banking industries in China.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".