Global assessment of the COVID-19 impact on IFRS 9 loan loss provisions
Bibliographic record
Abstract
Purpose This article aims to analyze the impact of COVID-19 measures by governments and central banks on International Financial Reporting Standards (IFRS) 9 loan loss provisions (LLPs). Changes in the total amount of LLPs, distribution of outstanding loan balance among IFRS 9 stages and credit risk parameters used for calculation are investigated for each world region where banks report under IFRS. Design/methodology/approach Data for a global selection of 105 banks reporting under IFRS were collected from 2019 to 2020 annual reports, financial statements, and Pillar III reports. These data provide the basis to empirically analyze the impact of COVID-19 on LLPs. Findings In most world regions Stage 2 balances increase while Stage 3 balances remain comparatively stable. The credit risk parameters used for computing LLPs remained stable in 2020. However, in China, the impact of COVID-19 on banks was not detected. Mean Stage 1 balances for Chinese banks increased slightly during the pandemic. Aside from the COVID-19 impact, we find that LLPs, credit risk parameters, and loss absorption capacities are significantly lower for banks in Canada, Oceania and Western Europe compared to those in the rest of the world. Originality/value There exists previous research examining the COVID-19 impact on financial stability, implementation of emergency rules and country-wide analyses to anticipate default rates depending on recovery scenarios. However, this is the first global study on the immediate impact of COVID-19 on LLPs. It reveals the significant differences between world regions and provides implications about their resilience against future credit shocks.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".