Modelling a sustainable credit score system (SCSS) using BWM and fuzzy TOPSIS

Sustainable development has emerged as a critical agenda for all organisations around the world. Despite the fact that profitability and sustainability are inorganically linked to financial institutions, sustainable lending has been a constant focus of attention. Due to regulatory pressure and stakeholder concerns, financial institutions are forced to implement a variety of sustainable measures; they are also gradually thinking to give more support to socially impactful and sustainable projects. Financial institutions can play a major role in establishing sustainable development by adopting the green lending policy. However, there have been few studies on sustainability credit score systems (SCSS) that take into account social and environmental factors. To fill the gaps of existing literature, this study proposes a multi-criteria SCSS that takes into account the environment and social aspects in addition to financial and managerial aspects. A combined Best-Worst Method (BWM) and the fuzzy-Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are used in this study to create a credit scoring system. The BWM is used to determine the weight of factors, and the fuzzy-TOPSIS is used to evaluate applicants. The ambiguity while evaluating borrowers has been captured by applying fuzzy set theory. A real-life case study is used to demonstrate the efficacy of the proposed model. The model is unique in terms of the number of social and environmental factors considered. This research will assist financial institutions in identifying borrowers who engage in sustainable business practices. Borrowers can be holistically prioritised by applying the model.