ABSTRACT

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.

摘要

可持续发展已成为全球所有组织的重要议程。尽管盈利能力和可持续性与金融机构存在无机联系，但可持续贷款一直是人们关注的焦点。由于监管压力和利益相关者的担忧，金融机构被迫实施各种可持续措施；他们还逐渐考虑为具有社会影响力和可持续发展的项目提供更多支持。金融机构可以通过采取绿色贷款政策在建立可持续发展方面发挥重要作用。然而，很少有关于考虑社会和环境因素的可持续性信用评分系统（SCSS）的研究。为了填补现有文献的空白，本研究提出了一个多标准 SCSS，除了财务和管理方面外，还考虑了环境和社会方面。在本研究中使用组合的最佳-最差方法 (BWM) 和通过与理想解决方案 (TOPSIS) 方法相似的订单偏好的模糊技术来创建信用评分系统。BWM用于确定因素的权重，Fuzzy-TOPSIS用于评估申请人。通过应用模糊集理论已经捕捉到评估借款人时的模糊性。一个真实的案例研究用于证明所提出模型的有效性。该模型在考虑的社会和环境因素的数量方面是独一无二的。这项研究将帮助金融机构识别从事可持续商业实践的借款人。