Computational Decision-Making under Uncertainty: A Grey-Fuzzy System for Strategic Bank Branch Evaluation

Abstract

In an era marked by digital transformation and complex socio-economic environments, decision-making in banking demands adaptive and interpretable computational tools. This study presents a hybrid decision-support framework that integrates two-stage grey data envelopment analysis with a fuzzy inference system to evaluate and guide the strategic management of bank branches under uncertainty. In the first stage, the two-stage grey data envelopment analysis calculates interval efficiency scores to reflect operational performance in uncertain data contexts. The second stage utilizes a fuzzy inference system that employs eight input criteria to classify branches into strategic actions: closure, neutral, or strengthening. This computationally grounded approach not only enhances transparency and consistency in decision-making but also aligns with the growing need for explainable data-driven strategies in organizational and policy settings. The findings confirm the model's ability to support complex, multi-criteria decisions in dynamic socio-technical systems, offering implications for both researchers and practitioners working at the intersection of digital technologies and social science applications.