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Loan Default Prediction in Ukrainian Retail Banking

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  • Goriunov Dmytro
  • Venzhyk Katerina

Abstract

Using a large proprietary dataset provided by the tenth largest Ukrainian banking institution, we posit reasons for loan defaults within two major groups of retail borrowers; car loans and mortgages. Two model types were used, namely logistic regression and neural networks. The results of our estimations suggest that a) data currently collected by banks are sufficient to predict defaults, but bankers should collect more information, and that b) the neural networks model slightly outperforms the logit model in predictive power.

Suggested Citation

  • Goriunov Dmytro & Venzhyk Katerina, 2013. "Loan Default Prediction in Ukrainian Retail Banking," EERC Working Paper Series 13/07e, EERC Research Network, Russia and CIS.
    • Handle: RePEc:eer:wpalle:13/07e
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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