The Impact of the Implementation of Financial Risks Management on the Disclosure Quality of Financial Reports
Bibliographic record
Abstract
This study aims to demonstrate the impact of banking risk management on the quality of disclosure in the financial reports of a sample of Lebanese commercial banks by addressing the subject in its theoretical and practical framework and its relationship with internal and external auditing. To achieve this goal, the study relied on the descriptive and analytical approach, and a sample of directors of the risk management department and directors of internal audit departments in the banks under study was selected, in addition to a sample of account auditors affiliated with the Association of Certified Public Accountants in Lebanon. For the purposes of statistical processing, appropriate statistical methods were used and the statistical analysis necessary to complete the current study was carried out, using the Statistical Package for Social Sciences (SPSS) program to process the data and extract the frequencies, arithmetic means and standard deviations according to the study page, and to test the study hypotheses at a significant level (0.05 ≥α). The study found a positive correlation between the level of disclosure of financial risk and each of currency risk, interest risk, credit risk, market risk and operational risk, in addition to the existence of a close relationship between audit and risk management, and is reflected in the quality of disclosure so that the efficiency and effectiveness in banking work leads to controlling these risks and avoiding them in the future.
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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 | 0.014 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".