An integrated fuzzy credit rating model using fuzzy-BWM and new fuzzy-TOPSIS-Sort-C

Abstract Financial institutions use credit rating models to make lending, investing, and risk management decisions. Credit have been developed using a variety of statistical machine learning methods. These methods, however, are data-intensive dependent on assumptions about data distribution. This research offers an integrated fuzzy model address such issues. study proposes reduce problems. The applies the best–worst method (fuzzy-BWM) obtain weight criteria that affect creditworthiness technique for order preference by similarity ideal solution (fuzzy-TOPSIS)-Sort-C evaluate borrowers. BWM was found consistent amongst existing multi-criteria decision-making (MCDM) consistency further improves when is extended version. TOPSIS-Sorting along with theory overcome human uncertainty while making decision. TOPSIS-sorting has capable handling rank reversal problems persist in TOPSIS method. fuzzy-TOPSIS-Sort-C applied borrowers based characteristic profile identified criteria. proposed model's efficacy illustrated case rate fifty firms real-life data. results compared previous studies commercially available ratings. show better accuracy terms true-positive rates predict default. It can help financial find potential granting credit.