Credit Risk Scoring in Entrepreneurship: Feature Selection

Mirjana Pejić Bach (), Natasa Sarlija, Jovana Zoroja, Bozidar Jakovic and Dijana Cosic
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Natasa Sarlija: Ekonomski fakultet Zagreb, Croatia
Jovana Zoroja: Ekonomski fakultet Zagreb, Croatia
Bozidar Jakovic: Ekonomski fakultet Zagreb, Croatia
Dijana Cosic: Wealthengine, Washington, DC, USA

Managing Global Transitions, 2019, vol. 17, issue 4 (Winter), 265-287

Abstract: 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.

Keywords: data mining; credit scoring; variable selection; decision tress; classification (search for similar items in EconPapers)
JEL-codes: C61 E51 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.26493/1854-6935.17.265-287

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