Assessing the Creditworthiness of Lebanese Banks Using Bayesian Networks
Bibliographic record
Abstract
This research evaluates the creditworthiness of Lebanese banks using the Bayesian Naïve Classifier (BNC) in the CAMELS framework. Using the CAMELS indicators—capital adequacy, asset quality, management efficiency, earnings, liquidity, and sensitivity to market risk—the study examines data from 2012 to 2022, a period also marked by financial instability. The complex interdependencies between these variables are modeled using the BNC, a machine learning technique that provides a probabilistic approach that improves prediction accuracy. In order to assess how well the BNC predicts banks’ ratings, training and testing datasets are created. The findings indicate that the most important elements influencing bank ratings are capital adequacy, management efficiency, and asset quality. Liquidity and sensitivity to market risk become more significant during economic downturns, especially following the 2019 financial crisis in Lebanon. With a predicted accuracy of more than 98%, the BNC proved its resilience and dependability in identifying patterns that traditional models would miss. By incorporating machine learning into the CAMELS framework, this study presents an innovative approach to credit risk assessment and offers insightful information to investors, regulators, and decision-makers who are keeping an eye on the stability of financial institutions. To further confirm this model’s resilience in many economic contexts, future studies should extend its use to more industries and geographical areas.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".