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.

Publication History
Avaliable online 27 December 2016
6691
views
3242
downloads
Full Text
Citation
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
Citation Format

Metrics
References

Abbas, O. A. (2008). Comparisons Between Data Clustering Algorithms. International Arab Journal of Information Technology, 5(5), 320-325.

Ayadi, R., Arbak, E., de Groen, W. P. (2011). Business models in European Banking: A pre- and post- crisis screening. Center for European Policy Studies. https://doi.org/10.2139/ssrn.1945779

Ayadi, R., Arbak, E., de Groen, W. P. (2012). Regulation of European Banks and Business models: Towards a New Paradigm? Center for European Policy Studies.

Ayadi, R., de Groen, W. P., Lapointe, M., Michelet, A., Rey, H., Sassi, I., Tita, C. (2014). Banking Business Models Monitor 2014. Center for European Policy Studies.

Ayadi, R., de Groen, W. P., Rey, H., Sassi, I., Mathlouthi, W., Aurby, O. (2015). Banking Business Models Monitor 2015. Center for European Policy Studies. https://doi.org/10.2139/ssrn.2784334

Bação, F., Lobo, V., Painho, M. (2005) Self-organizing maps as substitutes for k-means clustering. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, 3516, 476-483. Berlin: Springer. https://doi.org/10.1007/11428862_65

BIS (2016). Minimum Capital Requirements for Market Risk. Retrieved from https://www.bis.org/bcbs/publ/d457.pdf

Bullinaria, J. (2016). Self-Organizing Maps: Fundamentals, Introduction to Neural Computations.

Deboeck, G., Kohonen, T. (1998). Visual Explorations in Finance with Self-Organizing Maps. London: Springer-Verlag. https://doi.org/10.1007/978-1-4471-3913-3

European Banking Authority (2014). Guidelines on Common Procedures for the Supervisory Review and Evaluation Process (SREP) and Supervisory Stress Testing.

European Central Bank (2016). Financial Stability Review May, Recent trend in Euro Area Banks' Business Models.

Ferstl, R., Seres, D. (2012). Clustering Austrian Banks' Business Models and Peer Groups in the European Banking Sector. Financial Stability Report, 24, 79-95. Retrieved from https://www.oenb.at/Publikationen/Finanzmarkt/Finanzmarktstabilitaetsbericht/2012/Financial-Stability-Report-24.html

Halaj, G., Zochowski, D. (2009). Strategic groups and banks' performance. Financial Theory and Practice, 33(2), 153-186. Retrieved from https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=62559

Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69. https://doi.org/10.1007/BF00337288

La Porta, R., Lopez-de-Silanes, F., Zamarripa, G. (2001). Related lending. Working Paper, 8848. National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w8848.pdf

Mingoti, S. A., Lima, J. O. (2006). Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research, 174(3), 1742-1759. https://doi.org/10.1016/j.ejor.2005.03.039

Roengpitya, R., Tarashev, N., Tsatsaronis, K. (2014). Bank business models. BIS Quarterly Review, December. Bank for International Settlements. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1412g.pdf

Sarlin, P., Peltonen, T. (2011). Mapping the state of financial stability. Working Papers Series, 1382. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1382.pdf

Thorndike, R. (1953). Who belongs in the family? Psychometrika, 18(4), 267-276. https://doi.org/10.1007/bf02289263

Tomkus, M. (2014). Identifying Business Models of Banks: Analysis of Biggest Banks from Europe and United States of America. Aarhus University, Denmark.

Vagizova, V., Luire, K., Ivasiv, I. (2014). Clustering of Russian banks: business models of interaction of the banking sector and the real economy. Problems and Perspectives in Management, 12(1), 72-82.

Zarutska, O. (2012). Obgruntuvannya pidhodu mashtabnogo rozpodilu bankiv Ukrayni na osnovi structurno-funktsyonalnih grup. Visnyk of the National Bank of Ukraine, 10, 20-24. Retrieved from https://old.bank.gov.ua/doccatalog/document?id=125161

Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Submit Your Paper