Impact of Bank Deposits and Loans on Increasing Profit After Tax: A Comparative Study for Some Iraq Commercial Banks
Bibliographic record
Abstract
This study aims at clarifying the importance of auditing banking performance in measuring the impact of changes in bank loans and deposits to a sample of commercial banks in Iraq. As a comparative study, only three commercial banks are: Iraqi Investment Bank, Al-Mansour Investment Bank and Mosul Bank for Development and Investment because of the possibility of obtaining its annual reports and also they are characterized by transparency comparison with others. The methodology of the study is based on the quantitative and comparative approach to test the impact of loans and deposits on net profits after tax for the selected sample. The study hypotheses are tested based on data of the loans and deposits on net profits after tax for each bank for the period 2006-2020. According the results obtained from the testing the hypothesis of the study using statistical approaches, it was shown that the loans variable has not significant effect on increasing the net profits after tax for all banks, while, it was proved that there is clear effect to the deposit variable on increasing the net profits after tax for only two of three of targeted banks which are: the Al-Mansour Investment Bank and Mosul Bank for Development and Investment. It was recommended to consider the correct financial policy or the auditing banking performance followed by two banks above for exploiting and investing bank deposits.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".