A Multi-Criteria Decision-Making Approach to Improve Criteria Ranking and Weighting: Integrating SITDE Weighting With PIV and RAM Techniques

Skewed data distributions present a major challenge to the accuracy of Multi-Criteria Decision Making (MCDM) methods in financial performance evaluation. Traditional weighting techniques often neglect skewness, leading to biased and unreliable results. This research proposed an sophisticated MCDM approach that enhances the accuracy of the criteria rankings and weighting using Skewness-Insensitive Technique for Data Evaluation (SITDE) weighting combined with Preference Index Value (PIV) and Range of Alternatives Method (RAM) methods. Global bank rankings is obtained using the PIV and RAM MCDM techniques, with final rankings derived through the Borda Count to aggregate results. The findings indicate that DBS Bank, Bank of America, and Societe Generale obtained the highest overall ranking, reflecting superior performance across the applied techniques. By addressing data skewness and enhancing the robustness of the evaluation process, this approach offers a more accurate and adaptive solution for financial decision-making. The integration of SITDE weighting with PIV and RAM represents a meaningful advancement in the application of MCDM methods for global bank performance assessment.