Ukrainian Banks’ Business Models Clustering: Application of Kohonen Neural Networks
a SD Capital
b National Bank of Ukraine, Kyiv, Ukraine
Abstract

This paper clusters and identifies six distinct bank business models using Kohonen Self-Organising Maps. We show how these models transform over the crisis and conclude that some of them are more prone to default. We also analyze the risk profiles of the bank business models and differentiate between safest (valid) and riskiest ones. Specifically, six risk types (Profitability, Credit, Liquidity, Concentration, Related parties lending, and Money Laundering) are used to build risk maps of each business model. The method appears to be an efficient default prediction tool, since a back-testing exercise reveals that defaulted banks consistently find their place in a "risky" region of the map. Finally, we outline several potential fields of application of our model: development of an Early Warning System, Supervisory Review and Evaluation Process, mergers and acquisitions of banks.

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Avaliable online 27 December 2016
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Cite as: Rashkovan, V., Pokidin, D. (2016). Ukrainian Banks’ Business Models Clustering: Application of Kohonen Neural Networks. Visnyk of the National Bank of Ukraine, 238, 13-38. https://doi.org/10.26531/vnbu2016.238.013
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