A robust methodology for multi-criteria group decision-making: intuitionistic fuzzy N-bipolar soft expert sets in cybersecurity risk assessment for financial institutions

Cybersecurity risk assessment in financial institutions has become more difficult because cyber threats keep changing, many evaluation factors must be considered, and decisions often depend on the opinions of several experts. To deal with these challenges, an intuitionistic fuzzy N-bipolar soft expert set (IFNBSES) model is introduced. This framework integrates intuitionistic fuzzy concepts with N-bipolar soft expert sets (NBSESs). The IFNBSES model considers both degrees of acceptance and rejection, includes positive and negative evaluations, and allows experts to provide multi-level assessments. The model also provides a clear way to combine expert opinions when there is uncertainty. The IFNBSES model is formally defined, and its structure is explained using practical examples. Basic operations of the model are studied, and their algebraic properties are examined to ensure both theoretical soundness and practical usefulness. The model is then applied to multi-criteria group decision-making (MCGDM) problems to show how it supports clear, reliable, and well-informed selection of cybersecurity strategies. The results are discussed along with their implications for researchers and decision-makers, and the robustness of the proposed approach is evaluated. Finally, a comparison with existing methods shows that IFNBSES handles uncertainty more effectively, making it a strong tool for cybersecurity decision-making (DM).