An extension of complex fuzzy inference system for alert earlier credit risk at business banks

The purpose of this study is to suggest a novel integrated model for assessing credit risk at commercial banks that is based on a complex fuzzy transfer learning framework. Research Design and Methodology: We used transfer learning on a complex fuzzy inference system, complex fuzzy set theory, and a complex fuzzy inference system to build a credit risk prediction model. Parallel to this, we compared the proposed model with the previously used credit risk prediction method known as the Mamdani CFIS model. Results: The study has validated the complex fuzzy inference model's capacity to accurately predict credit risk. When compared to the Mamdani CFIS model, the suggested model exhibits superior time performance. In particular, the time needed to construct the intricate fuzzy inference system and to carry out inference in the suggested model is much reduced when compared to the Mamdani CFIS. Conclusion: In addition to elucidating the role and possibilities of complex fuzzy inference systems, this work shows that the transfer learning model on complex fuzzy inference systems may significantly accelerate the prediction of credit risk. This is especially important in the context of early warning, which enables commercial banks to implement more efficient risk prevention and management strategies.