Application of Fuzzy Multi-Criteria Decision Making in Financial Decision Making

Abstract

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.

Introduction

In today’s complex and uncertain financial environment, decision-making involves multiple conflicting criteria—such as return, risk, liquidity, and volatility—often under conditions of imprecise data and subjective judgment. Traditional Multi-Criteria Decision Making (MCDM) models fall short in handling such ambiguity. To address this, fuzzy logic has been integrated into MCDM frameworks, creating Fuzzy MCDM techniques that better accommodate linguistic variables, uncertainty, and expert opinions. Methods like Fuzzy AHP, Fuzzy TOPSIS, and Fuzzy VIKOR have been applied to key financial areas including portfolio optimization, credit risk, performance evaluation, and capital budgeting.

The literature shows progressive developments from early fuzzy portfolio models to sophisticated hybrid methods and recent advances like Interval Type-2 Fuzzy MCDM, which improve handling of uncertainty and ambiguity in financial decisions. Despite extensive application, gaps remain in integrating higher-order fuzzy sets and applying these models to emerging fields like ESG and fintech.

Applications of fuzzy MCDM span various financial decision domains: investment portfolio selection, credit risk assessment, capital budgeting, financial performance evaluation, fintech and ESG analysis, risk management, corporate finance, and consumer finance. These methods enhance decision quality by incorporating both quantitative data and qualitative expert judgments under uncertainty.

The study reviewed 36 years of research, proposed a comparative framework for fuzzy MCDM techniques, and introduced a novel Interval Type-2 Fuzzy Decision Support System to better support real-world financial decisions amid uncertainty.

Conclusion

This study confirms that Fuzzy MCDM techniques serve as powerful tools in the domain of financial decision-making, effectively bridging the gap between quantitative metrics and qualitative insights. By incorporating fuzzy logic, these models can accommodate uncertainty, vagueness, and imprecision—challenges frequently encountered in real-world financial scenarios. Applications ranging from investment selection to risk assessment show that Fuzzy MCDM enhances the objectivity and transparency of decisions while supporting better stakeholder understanding and confidence. Moreover, hybrid models that integrate methods such as Fuzzy AHP, Fuzzy TOPSIS, and Fuzzy GRA prove to be especially effective in handling multidimensional financial evaluation problems. Future research should focus on the development of dynamic, real-time fuzzy decision-support systems tailored to specific financial domains like fintech, ESG investing, and blockchain-based financial platforms. Future research can explore the integration of Interval Type-2 fuzzy sets and intuitionistic fuzzy logic to better model decision-maker hesitation and higher levels of uncertainty. Developing real-time, AI-enhanced fuzzy decision-support systems (DSS) tailored to specific financial sectors such as fintech, cryptocurrency, and ESG investing is essential. There is also scope for applying hybrid fuzzy MCDM with machine learning to improve predictive financial analytics. Furthermore, creating standardized frameworks and open-access datasets can help validate and benchmark fuzzy MCDM models across various financial contexts.

References

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Copyright

Copyright © 2025 Dr. J. Pandima Devi, Dr. S. Sripriya, D. Amutha, M. Selvaraj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.