Exposure at default: drivers for Canadian cooperative secto
Bibliographic record
Abstract
Defaults by individuals were at the source in the last financial crisis, thus the need to fully understand credit risk from personal borrowers. Expected loss from credit is usually decomposed in probability of default, loss given default and exposure at default (EAD), the latter factor being yet the least investigated. This research seeks to contribute by identifying the determinants of EAD in the Canadian financial cooperative sector that had exhibited great resiliency during the crisis. The sample consisted of more than 11000 cases of default occurring between 2003 and 2008 on revolving lines of credit granted to individuals. The results show that several factors are significant, namely the borrower’s age, the exposure limit, the amount drawn, the interest rate applied on the line of credit and the utilization behavior. Moreover, the relationship of EAD to macroeconomic factors points to it. Overall, more than 50% of the variance of EAD can be explained. In sum, the research sheds light on a credit factor, EAD on credits to individuals, which has remained rather obscure up to now. The improved understanding of EAD can lead to better risk modeling, better credit management and, potentially, improve financial stability.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".