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Credit Risk Scoring in Entrepreneurship: Feature Selection

Author

Listed:
  • Mirjana Pejic Bach

    (Ekonomski fakultet Zagreb, Croatia)

  • Natasa Sarlija

    (Ekonomski fakultet Zagreb, Croatia)

  • Jovana Zoroja

    (Ekonomski fakultet Zagreb, Croatia)

  • Bozidar Jakovic

    (Ekonomski fakultet Zagreb, Croatia)

  • Dijana Cosic

    (Wealthengine, Washington, DC, USA)

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.

Suggested Citation

  • Mirjana Pejic Bach & Natasa Sarlija & Jovana Zoroja & Bozidar Jakovic & Dijana Cosic, 2019. "Credit Risk Scoring in Entrepreneurship: Feature Selection," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 17(4 (Winter), pages 265-287.
    • Handle: RePEc:mgt:youmgt:v:17:y:2019:i:4:p:265-287
    DOI: 10.26493/1854-6935.17.265-287
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    More about this item

    Keywords

    data mining; credit scoring; variable selection; decision tress; classification;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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