Ukrainian Banks’ Business Models Clustering: Application of Kohonen Neural Networks
Journal Title: Visnyk of the National Bank of Ukraine - Year 2016, Vol 0, Issue 238
Abstract
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.
Authors and Affiliations
Dmytro Pokidin, Vladyslav Rashkovan
Keywords
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- EP ID EP426545
- DOI 10.26531/vnbu2016.238.013
- Views 119
- Downloads 0
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