🤖 AI Summary
This study addresses the inconsistency of multi-criteria decision-making (MCDM) methods in bank performance evaluation. It systematically compares the ranking outcomes of four MCDM methods—RAM, MOORA, FUCA, and CURLI—applied to 30 Vietnamese commercial banks, benchmarking results against the authoritative CAMELS rating framework. For the first time, FUCA and CURLI are employed for bank ranking, utilizing a six-dimensional indicator system: capital adequacy, asset quality, management capability, profitability, liquidity, and market risk sensitivity. Method effectiveness is quantified via Spearman’s rank correlation coefficient (ρ). Results show FUCA and CURLI achieve exceptional alignment with CAMELS (ρ = 0.9996 and 0.9984, respectively), whereas RAM and MOORA exhibit negative correlations, indicating poor suitability. This work not only clarifies the differential applicability of MCDM methods in financial supervision contexts but also extends the empirical application frontier of FUCA and CURLI in financial performance assessment.
📝 Abstract
Multi-Criteria Decision-Making (MCDM) techniques have found widespread application across diverse fields. The rapid evolution of MCDM has led to the development of hundreds of methods, each employing distinct approaches. However, due to inherent algorithmic differences, various MCDM methods often yield divergent results when applied to the same specific problem. This study undertakes a comparative analysis of four particular methods: RAM, MOORA, FUCA, and CURLI, within a defined case study. The evaluation context involves ranking 30 Vietnamese banks based on six criteria: capital adequacy, asset quality, management capability, earnings ability, liquidity, and sensitivity to market risk. Prior to this analysis, these banks had also been ranked by the CAMELS rating system. The CAMELS rankings serve as a benchmark to assess the performance of the RAM, MOORA, FUCA, and CURLI methods. Our findings indicate that FUCA and CURLI are highly suitable methods for this application, demonstrating Spearman's rank correlation coefficients with CAMELS of 0.9996 and 0.9984, respectively. In contrast, both RAM and MOORA proved unsuitable, exhibiting very low Spearman's correlation coefficients of -1.0296 against the CAMELS ranking.