Credit Risk Evaluation Using Clustering Based Fuzzy Classification Method

Başer F., Koç O., Kestel A. S.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.223, sa.119882, ss.1-26, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 223 Sayı: 119882
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.eswa.2023.119882
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-26
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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