Developing a sustainable credit decision system (SCDS) to identify sustainable borrowers for sustainable investment
研究提出一个基于模糊BWM和TOPSIS-Sorting的可持续信贷决策系统，帮助金融机构识别可持续借款人，实证显示准确率78.43%，环境和社会变量最关键。
The green economy balances the economy, nature, and environment to secure a better future for civilisation. Financial institutions (FIs) can play a significant role as stakeholders by encouraging advances in socially important, relevant, and sustainable activities toward a green economy. However, FIs face significant challenges in identifying sustainable borrowers amongst lots. To address the concerns, this research presents a sustainable credit decision system (SCDS) using a fuzzy best-worst method (BWM) and the recently extended fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) -Sorting. The usefulness of the purported system has been evidenced with an empirical case study. According to the study’s results, environmental and social variables are most significant in identifying sustainable borrowers. The proposed system has shown an accuracy rate of 78.43% with a true positive rate (TPR) of 87.5% and a false positive rate (FPR) of 23.25% against comparable outcomes by the financial-based model. Policymakers in banks, FIs and regulators may refer to the system to include various sustainable criteria in their lending process.