Evaluating the challenges of digital transformation in sustainable financial services using an integrated Pythagorean fuzzy MACONT model

Purpose As the thrust on sustainable development gains momentum, the financial sector has emerged as the prime mover to drive sustainability. Sustainable finance is a powerful tool that aims to align investments and financial services with sustainable development goals. Owing to the association of various factors, the assessment of the digital transformation (DT) challenges in sustainable finance services can be treated as a multi-criteria group decision-making problem. This paper aims to introduce a hybrid Pythagorean fuzzy (PF)-based decision-making model for assessing DT challenges in sustainable financial services (SFSs). Design/methodology/approach On the basis of the proposed score function and distance measure, we present the stepwise algorithm of the introduced ranking framework in which the weights of decision experts are derived using the score function and rank reciprocal-based procedure. Further, the developed model determines the impacts of DT challenges by combining the objective weights through the symmetry point of the criterion tool and the subjective weights by relative closeness coefficient-based approach within the context of PF sets. As per the decision experts and DT challenges’ impacts, a hybrid PF mixed aggregation by a comprehensive normalization technique approach is presented to evaluate the SFS systems. Findings The results of the study prove that environmental, social and governance finance is the most appropriate choice by means of DT challenges. Sensitivity analysis is conducted to analyze the impacts of different parameters on the final result. Finally, a comparison with extant methods including TOPSIS, VIKOR, Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) is provided to test the validity of the introduced model. Originality/value To develop the proposed method, we first present a new score function for PF number. Some numerical examples are discussed to show its advantages over existing PF-score functions. Next, we develop a modified distance measure for describing the discrimination degree between PF sets and further highlight its effectiveness by comparing it with existent PF-distance measures.