Loan Default Prediction in Ukrainian Retail Banking

Goriunov Dmytro () and Venzhyk Katerina ()

EERC Working Paper Series from EERC Research Network, Russia and CIS

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.

JEL-codes: G21 G32 G33 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ban, nep-dcm and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:eer:wpalle:13/07e

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