PENERAPAN FUZZY LOGIC DALAM PENENTUAN TINGKAT KELAYAKAN KREDIT NASABAH

Abstract

Credit granting by financial institutions requires accurate assessment of customer creditworthiness. In the presence of uncertain data and qualitative judgments, conventional methods often struggle to handle ambiguous variables. This article reviews the application of fuzzy logic in assessing customer credit eligibility. Through a literature review, several fuzzy-based models are examined, including fuzzy inference systems, fuzzy-BWM, fuzzy-TOPSIS and fuzzy-neural hybrids, as applied in credit scoring and credit risk evaluation. The findings indicate that fuzzy methods are more capable of managing uncertainty and ambiguity compared to classical statistical methods, offering flexibility for modeling qualitative criteria such as character, repayment capacity, and macro‐economic conditions. The discussion highlights advantages, limitations, and implications for banking practice. In conclusion, applying fuzzy logic in credit decision-making presents a significant potential to improve the accuracy and transparency of customer credit evaluations.