Computational Intelligence for Sustainable Banking: A Novel Fermatean Fuzzy LOPCOW–EDAS Framework

The primary objective of this study is to identify the priority strategies required for banks to achieve their sustainable growth targets and to develop a new fuzzy multi-criteria decision-making model sensitive to uncertainty conditions. The model proposed in this study is designed based on the integration of Fermatean Fuzzy LOPCOW–EDAS. In the first stage, the criteria and strategic alternatives affecting sustainable growth were identified through a literature review. The LOPCOW method was then used to objectively calculate the criteria's importance weights. The prioritization of strategic alternatives was then performed using the EDAS method. To more accurately model the uncertainties in expert judgments, the opinions of ten experts were converted to the Fermatean fuzzy numbers and analyzed. The use of Fermatean fuzzy sets offers greater expressive power and increases decision reliability compared to traditional fuzzy and Pythagorean approaches. The LOPCOW method objectively evaluates the information density of the criteria by using logarithmic percentage change, while the EDAS method reduces the impact of outliers by considering the distance of the alternatives from the mean solution, producing a more stable ranking. The findings indicate that the "digital green banking practices" criterion is the most critical element for sustainable growth. Furthermore, the "Digitalization and innovation capability" strategy was determined to be the most important alternative. This result demonstrates that sustainable growth in the banking sector can be achieved through the integration of digital technologies and environmentally friendly practices.

@article{Majidah2025,
  title     = {Computational Intelligence for Sustainable Banking: A Novel Fermatean Fuzzy LOPCOW–EDAS Framework},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Majidah Majidah and Dadan Rahadian and Anisah Firli and Suhal Kusairi and Serkan Eti and Serhat Yüksel and Hasan Dinçer},
  doi       = {10.14569/IJACSA.2025.0161269},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161269}
}