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Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines

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  • Nehrebecka Natalia

    (National Bank of Poland, University of Warsaw, Warsaw, Poland; nnehrebecka@wne.uw.edu.pl)

Abstract

The aim of the article is to compare models on a train and validation sample, which will be created using logistic regression and Support Vector Machine (SVM) and will be used to assess the credit risk of non-financial enterprises. When creating models, the variables will be subjected to the transformation of the Weight of Evidence (WoE), the number of potential predictions will be reduced based on the Information Value (IV) statistics. The quality of the models will be assessed according to the most popular criteria such as GINI statistics, Kolmogorov-Smirnov (K-S) and Area Under Receiver Operating Characteristic (AUROC). Based on the results, it was found that there are significant differences between the logistic regression model of discriminatory character and the SVM for the model sample. In the case of a validation sample, logistic regression has the best prognostic capability. These analyses can be used to reduce the risk of negative effects on the financial sector.

Suggested Citation

  • Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
    • Handle: RePEc:vrs:eaiada:v:22:y:2018:i:2:p:54-73:n:5
    DOI: 10.15611/eada.2018.2.05
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    References listed on IDEAS

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    Cited by:

    1. Lukasz Prorokowski, 2022. "New definition of default," Bank i Kredyt, Narodowy Bank Polski, vol. 53(5), pages 523-564.
    2. Guner Altan & Server Demirci, 2022. "Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 397-424, July.
    3. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.

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    More about this item

    Keywords

    Basel III; Internal Rating Based System; credit scoring; Support Vector Machines; logistic regression;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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