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Decades ago, a bank thoroughly utilized experts to determine financial decisions based on their assessments by evaluating risks qualitatively against the company’s financial statements, business scenarios, and individual or conventional client interview results. However, this provoked a relatively subjective evaluation process and faced more complex criteria conditions with specific requirements. In addition, policymakers continued to use uncertain information in the evaluation process despite the increasing concerns over inconsistency, indeterminacy, and uncertainty in data that have emerged and become significant challenges in decision-making. Therefore, this paper introduces an integrated decision support framework based on spherical fuzzy numbers (SFNs) for overcoming a banking financial aid recipient evaluation. The proposed framework combines two developed algorithms, which are a spherical fuzzy number method based on the removal effects of criteria (SFN-MEREC) and a spherical fuzzy number ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (SFN-RAFSI) algorithm. The SFN-MEREC is used to determine objective criteria weight coefficients, whereas the SFN-RAFSI is used to select the appropriate banking financial aid recipient. The proposed framework exhibits the results in line and a positive correlation with other spherical fuzzy decision-making methods authenticated through comparative analysis and weighted Spearman’s rank correlation coefficient evaluations. Finally, a rank reversal simulation analysis demonstrates the framework’s ability and stability in managing inconsistent, indeterminate, and uncertain data, ensuring its robustness throughout the evaluation process.
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