The Impact of Credit Risk Management on the Profitability of a Commercial Bank: The Case of BGFI Bank Congo
Bibliographic record
Abstract
This study examines the impact of credit risk management on the profitability of BGFI Bank Congo, by identifying credit risk indicators and profitability measurement ratios over the period of 2010-2019. The results indicate that profitability is somewhat affected by credit risk management as measured by its credit risk management indicators. The non-performing loan ratio (NPLR), the capital assets ratio (CAR), and the loan loss provision ratio (LLPR) show a negative impact on ROE. These three ratios contribute negatively, while the CAR makes a positive contribution to Return on assets (ROA) and the ratio of client loans and short-term financing (RCLSTF) on return on equity (ROE). Thus, credit risk management has a significant impact on profitability. The study also shows that other selected credit risk management indicators have a significant impact on the Bank's profitability, such as the loan provision ratio (LLPR) and the clean capital adequacy ratio.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".