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National Bank of Ukraine Econometric Model for the Assessment of Banks’ Credit Risk and Support Vector Machine Alternative

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  • Dmytro Pokidin

    (National Bank of Ukraine)

Abstract

Econometric models of credit scoring started with the introduction of Altman’s simple z-model in 1968, but since then these models have become more and more sophisticated, some even use Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques. This paper focuses on the use of SVM as a model for default prediction. I start with an introduction to SVM as well as to some of its widespread alternatives. Then, these different techniques are used to model NBU data on banks’ clients, which allows us to compare the accuracy of SVM to the accuracy of other models. While SVM is generally more accurate, I discuss some of the features of SVM that make its practical implementation controversial. I then discuss some ways for overcoming those features. I also present the results of the Logistic Regression (Logit) model which will be used by the NBU.

Suggested Citation

  • Dmytro Pokidin, 2015. "National Bank of Ukraine Econometric Model for the Assessment of Banks’ Credit Risk and Support Vector Machine Alternative," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 234, pages 52-72.
    • Handle: RePEc:ukb:journl:y:2015:i:234:p:52-72
    DOI: 10.26531/vnbu2015.234.052
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    File URL: https://journal.bank.gov.ua/en/article/2015/234/03
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    More about this item

    Keywords

    machine learning; svm; credit risk; scoring model;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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