Banking Risks in the Asset and Liability Management System
Bibliographic record
Abstract
Banking risk management is considered weak compared to rapid changes in financial markets. In light of the recent global financial crisis, banking risk management has become a significant concern of banking regulators and government agencies. This work aims to build a model for assessing banking risks. The primary study method is economic–mathematical modeling based on the standardized model of the Basel Committee for Operational Risk Management, the modified CAPM model, and the model developed by Shapiro and Cornell for currency risk management. The information base was the financial statements of Bank Credit Agricole (Poland). As a result, an economic–mathematical model is built, which is the optimal combination of operational, currency, and credit risk management models. This model calculates the optimal values of bank balance sheet items, which allows for making the right management decisions. It allowed adjusting the value of the bank profit by 3.6 million US dollars. In conclusion, considering the results of banking risk modeling, the need to build a strategy for the bank’s development is determined.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".