Developing a multi-criteria sustainable credit score system using fuzzy BWM and fuzzy TOPSIS

Sustainability has emerged as a dominating paradigm across global institutions as a critical component for their prospects. Organisations all across the globe must commit to strengthen the values as a participant in sustainable development. Financial institutions are no exception; they are also being pushed to undertake many sustainable initiatives, such as increasing socially meaningful, relevant, and sustainable projects. In addition, they may contribute to sustainable growth by enacting a green banking policy. To promote the green financing strategy, this study proposes a multi-criteria sustainable credit scoring model considering triple bottom line attributes (economic, environmental, and social) besides managerial attributes. The model is built on a novel hybrid approach that combines the fuzzy best–worst method (BWM) with the fuzzy technique for order preferences by similarity to an ideal solution (TOPSIS). The fuzzy BWM was used to weigh factors, while the fuzzy TOPSIS was utilised to evaluate borrowers. The integration of fuzzy set theory assisted in overcoming decision-making ambiguity. An empirical analysis was performed to demonstrate the utility of the proposed model. According to the study’s findings, the most important attribute for sustainable credit scoring is environmental and social sustainability and financial sustainability. On policy implications, regulators could also use the framework as a benchmark to counsel financial institutions on how to include different sustainable criteria into their credit lending process. Furthermore, financial institutions could use the proposed technique as a part of a sustainable lending policy to identify potential borrowers engaged in sustainable business.