Using Fuzzy Analytic Hierarchy Process and Technique for Order of Preference by Similarity to the Ideal Solution in Performance Evaluation in the Albanian Banking Sector
Bibliographic record
Abstract
The banking sector plays a key role in the economic, social, and political development of a country. The study of the financial performance of banks is essential for investors, creditors, and other interested parties. The aim of this research was to rank the second-tier banks in Albania by financial performance using a fuzzy multi-criteria decision model (fuzzy MCDM). For the ranking of banks, eight financial criteria were taken into account during the years 2020, 2021, and 2022 for 11 banks in the Albanian banking sector. Based on the selected indicators, a decision-making model was created. The Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) methods were used in this research. The results of the FAHP method showed that the most important indicators are Equity and EBT. The results of the TOPSIS method showed that Banka Kombëtare Tregtare (BKT) had the best indicators for the observed years. The contribution of this research is in understanding the financial operations of banks in Albania.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".