The second Basel accord was published in 2004 by the Basel Committee on Banking Supervision which allows financial institutions under surveillance to measure their own credit risk to define its own credit rating. Since then, credit scoring (CS) is a well-studied topic that relies on different classical statistics and machine learning algorithms to classify applicants as good/bad based on the probability of default of their own payments. This paper presents a literature review on the use of fuzzy theory in credit scoring models where the main goal is to show the main applications of fuzzy theory in credit scoring and its advantages for modeling imprecise information. This research is significant as it provides a comprehensive overview of the current state of fuzzy theory applications in credit scoring models, the most relevant authors and the trends in research. By synthesizing recent advancements in this area, it serves as a valuable resource for researchers and practitioners aiming to enhance model interpretability and mitigate risk, particularly in the context of financial crises. The insights offered herein will facilitate a deeper understanding of how fuzzy logic can improve decision-making processes in credit risk assessment.