Credit risk management: a survey of practices
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
Purpose Proposes to investigate the current practices of credit risk management by the largest US‐based financial institutions. Owing to the increasing variety in the types of counterparties and the ever‐expanding variety in the forms of obligations, credit risk management has jumped to the forefront of risk management activities carried out by firms in the financial services industry. This study is designed to shed light on the current practices of these firms. Design/methodology/approach A short questionnaire, containing seven questions, was mailed to each of the top 100 banking firms headquartered in the USA. Findings It was found that identifying counterparty default risk is the single most‐important purpose served by the credit risk models utilized. Close to half of the responding institutions utilize models that are also capable of dealing with counterparty migration risk. Surprisingly, only a minority of banks currently utilize either a proprietary or a vendor‐marketed model for the management of their credit risk. Interestingly, those that utilize their own in‐house model also utilize a vendor‐marketed model. Not surprisingly, such models are more widely used for the management of non‐traded credit loan portfolios than they are for the management of traded bonds. Originality/value The results help one to understand the current practices of these firms. As such, they enable us to make inferences about the perceived importance of the risks. The paper is of particular value to the treasurers intending to better understand the current trends in credit risk management, and to academics intending to carry out research in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it