In today’s volatile and complex financial environment, decision-makers are often confronted with conflicting objectives, uncertain data, and vague human judgments. Traditional Multi-Criteria Decision Making (MCDM) methods, while useful, struggle to handle the inherent ambiguity present in many financial contexts. This paper explores the integration of Fuzzy Logic with MCDM techniques—collectively known as Fuzzy MCDM—to enhance the robustness and accuracy of financial decision-making. Applications such as investment portfolio selection, credit risk evaluation, capital budgeting, and mutual fund performance assessment are examined through the lens of hybrid fuzzy methodologies like Fuzzy AHP, Fuzzy TOPSIS, and Fuzzy VIKOR. By incorporating both qualitative and quantitative criteria, Fuzzy MCDM models offer a flexible, data-driven, and linguistically interpretable approach for financial analysis. The study highlights case examples and comparative results that demonstrate how Fuzzy MCDM tools improve decision quality in environments characterized by uncertainty and subjective judgment
Jai Devi (Thu,) studied this question.