What drives the credit risk in the banking sector? A systematic literature review
Bibliographic record
Abstract
A stable banking sector is an essential part of the modern-day economy and higher credit risk is a major source of bank instability. Therefore, many research studies have been conducted to identify the determinants of credit risk in the past three decades. The purpose of this paper is to conduct a systematic literature review of the prior studies on the determinants of credit risk published from 1988 to 2022 in peer-reviewed journals. The motivations of this study are to draw a more comprehensive conceptual framework of credit risk determinants, evaluate the policy responses to recent banking crises and propose future research avenues. The findings of 452 relevant prior studies are divided into three broad categories of credit risk determinants. The three broad categories are macro, bank-specific and sector-specific variables which are then divided into six sub-categories and further divided into 43 credit risk determinations. The results reveal that more specific, multidiscipline and multicounty research studies may help to further understand this topic to improve stability in the banking sector. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".