EXPERT SYSTEMS WITH APPLICATIONS, cilt.223, sa.119882, ss.1-26, 2023 (SCI-Expanded)
Credit scoring is a crucial indicator for banks to determine the financial position and the eligibility of a
client for credit. In order to assign statistical odds or probabilities to predict the risk of nonpayment in
relation to many other factors, the scoring criterion becomes an important issue. The focus of this
study is to propose a clustering based fuzzy classification (CBFC) method for credit risk assessment. We
aim to illustrate the beneficial use of machine learning (ML) methods whose prediction power is
increased by adopting fuzzy theory to calculate the default risk with a better selection of the features
contributing to it. An important feature of the CBFC method is that membership values obtained as a
result of the fuzzy k-means clustering algorithm are used for the purpose of better capturing the
structure of an existing system.
An extensive comparison is performed to show how CBFC performs compared to the traditional ones
for the datasets having different characteristics in terms of the variable types. Five different real-life
datasets are studied to expose the contribution of fuzzy approach on improving the ML use. Our
findings show that the proposed CBFC models can produce promising classification results in credit
risk evaluation which aid the practitioners and decision makers for issuance of credit purposes