The impact of credit risk management on the financial performance of United Arab Emirates commercial banks
Bibliographic record
Abstract
The main purpose of this study is to measure up to what extent the independent factors defined by capital adequacy ratio, non-performing loans ratio, cost-income ratio, liquidity ratio, and loans-to-deposits ratio impact the financial performance of sixteen commercial banks operating in the United Arab Emirates using panel data for the period of 2013-2019. The secondary data was collected from banks and examined by applying standard descriptive statistics and the random effect model for hypothesis testing. It is concluded from the regression outcomes that non-performing loans ratio and cost-income ratio have a significant negative impact on commercial banks profitability in the United Arab Emirates, while capital adequacy ratio, liquidity ratio, and loans -to-deposits ratio all have a very weak positive relationship on the return on assets but they are not determinants of bank’s profitability due to the insignificant statistical impact on it. It is therefore suggested that to enhance financial performance and minimize the risk of non-performing loans in the future, banks must watch very carefully the loans’ performance and analyze thoroughly the clients’ credit history and ability to pay back their debts prior to any approval of loan applications. Furthermore, banks should continuously improve their assets utilization, liquidity, and techniques of managing operating costs, improve the impact of capital adequacy, and the use of deposits for lending activities from a weak positive impact to a significant positive impact on their profitability. The researchers recommend that future studies on credit risk management influence on banks’ financial performance should consider more independent variables and longer periods of study such as twenty or thirty years to have more accuracy and generalized results.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".