An artificial immune system for extracting fuzzy rules in credit scoring

Various credit scoring models have been proposed to estimate credit risk of loan applicants. Recently, the use of artificial immune systems (AIS) in credit problems has been increased. AIS is inspired from natural immune system which has the ability of determining self from non-self. The aim of this study is constructing an AIS-based model to extract fuzzy rules to predict the likelihood of customers such as good/bad payer. The rules have made our model human-understandable which helps experts to organize their knowledge from the domain. We use Weka data mining software to compare our classifier with several well-known classifiers. The evaluation criterias which have been used in this paper are average correct classification rate, precision, recall and f-measure. The experiments were performed on Australian and German Credit Approval datasets. The results demonstrate that proposed AIS-based classifier has high accuracy and interpretability which makes it competitive to several well-known classification systems.