Fuzzy Logic in Credit Risk Assessment: A Literature Review

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Perustieteiden korkeakoulu | Bachelor's thesis
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Date

2025-09-05

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Major/Subject

Data Science

Mcode

SCI3095

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

29+9

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Abstract

Credit risk assessment is a major aspect of the finance industry that affects the profitability and reputation of the lender, as well as the overall stability of financial systems. Traditional and statistical methods often fail when faced with the uncertain nature of complex problems in a real-world context. In contrast, fuzzy logic stands out as a potential option for financial institutions with its ability to embrace vagueness and subjectivity. This is significantly aligned with the human reasoning process. The aim of this thesis is to analyze existing methodologies and models for fuzzy logic applications to evaluate credit risks through a literature review. The chosen three models include a Sugeno model to predict consumer default rates with the data of 500 credit cases in a local Almaty bank; a proposed fuzzy expert system for assessing credit risk for Egyptian banks; and a neuro-fuzzy ANFIS approach to assess credit risk in Iranian banks. The thesis also highlights the potential of fuzzy systems in modern banking with their high interpretability and competence in handling imprecision. Finally, it also suggests directions for future research and progress in the industry, including optimizing costs and developing hybrid approaches, namely fuzzy simulation and probabilistic fuzzy methods.

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Supervisor

Korpi-Lagg, Maarit

Thesis advisor

Ferranti, Luca

Keywords

adaptive neuro-fuzzy system (ANFIS), artificial intelligence (AI), credit risk assessment, defuzzification, fuzzy c-means (FCM), fuzzy logic

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https://urn.fi/URN:NBN:fi:aalto-202510218381

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[kand] Perustieteiden korkeakoulu / SCI