Credit Risk Evaluation Using Clustering Based Fuzzy Classification Method

Date

2023-03-01

Author

Koç, Oğuz
Başer, Furkan
Kestel, Sevtap Ayşe

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Credit scoring is a crucial indicator for banks to determine the financial position and the eligibility of aclient for credit. In order to assign statistical odds or probabilities to predict the risk of nonpayment inrelation to many other factors, the scoring criterion becomes an important issue. The focus of thisstudy is to propose a clustering based fuzzy classification (CBFC) method for credit risk assessment. Weaim to illustrate the beneficial use of machine learning (ML) methods whose prediction power isincreased by adopting fuzzy theory to calculate the default risk with a better selection of the featurescontributing to it. An important feature of the CBFC method is that membership values obtained as aresult of the fuzzy k-means clustering algorithm are used for the purpose of better capturing thestructure of an existing system.An extensive comparison is performed to show how CBFC performs compared to the traditional onesfor the datasets having different characteristics in terms of the variable types. Five different real-lifedatasets are studied to expose the contribution of fuzzy approach on improving the ML use. Ourfindings show that the proposed CBFC models can produce promising classification results in creditrisk evaluation which aid the practitioners and decision makers for issuance of credit purposes

URI

https://reader.elsevier.com/reader/sd/pii/S0957417423003834?token=4ADA4C696AD7F8F4352F9EAF5347E70372D15F921997379A4D0014E66595287831E0182DFE8AC207258E0B2045640701&originRegion=eu-west-1&originCreation=20230317090441
https://hdl.handle.net/11511/102695

Journal

EXPERT SYSTEMS WITH APPLICATIONS

DOI

https://doi.org/10.1016/j.eswa.2023.119882

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

O. Koç, F. Başer, and S. A. Kestel, “Credit Risk Evaluation Using Clustering Based Fuzzy Classification Method,” EXPERT SYSTEMS WITH APPLICATIONS, vol. 223, no. 119882, pp. 1–26, 2023, Accessed: 00, 2023. [Online]. Available: https://reader.elsevier.com/reader/sd/pii/S0957417423003834?token=4ADA4C696AD7F8F4352F9EAF5347E70372D15F921997379A4D0014E66595287831E0182DFE8AC207258E0B2045640701&originRegion=eu-west-1&originCreation=20230317090441.