Credit Risk Scoring in Entrepreneurship: Feature Selection

• Main Authors: Mirjana Pejic Bach, Natasa Sarlija, Jovana Zoroja, Bozidar Jakovic, Dijana Cosic
• Format: Article
• Language: English
• Published: University of Primorska 2019-09-01
• Series: Managing Global Transitions
• Subjects: data mining; credit scoring; variable selection; decision tress; classification
• Online Access: http://www.hippocampus.si/ISSN/1854-6935/17.265-287.pdf

The goal of this research is to investigate the impact of different algorithms for the feature selection for the purpose of credit risk scoring for the entrepreneurial funding by the Croatian financial institution.We use demographic and behavioral data, and apply various algorithms for the development of classification model. In addition, we evaluate several algorithms for the variable selection, which are additionally based on the classification accuracy. Sequential Minimal Optimization algorithm in combination with the Class CfcSubsetEval and ConsistencySubsetEval algorithms for variable selection was the most accurate in predicting credit default, and therefore the most useful for the credit risk scoring.