Credit risk evaluation using clustering based fuzzy classification method

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 the promising classification results in credit risk evaluation which aid the practitioners and decision makers for issuance of credit purposes.

Automated summary

This study proposes a clustering based fuzzy classification (CBFC) method for credit risk assessment, which enhances the prediction power of machine learning methods by adopting fuzzy theory. Extensive comparisons are performed to demonstrate the performance of CBFC compared to traditional methods on datasets with different characteristics. The findings show that CBFC models can produce promising results in credit risk evaluation, aiding practitioners and decision makers in credit issuance.