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Market Segmentation with Self-Organizing Maps in Banking Industry

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  • Mehmet OZCALICI

Abstract

Banks play important roles in an economy with their financial intermediary roles. For this reason, supervision of the banking sector is also important. The development of the sector can be monitored with clustering analysis. In this study, self-organizing maps which implements unsupervised learning is applied to cluster 13 commercial banks with branches over 100. The dataset covers 2014-2017 period. 12 financial ratios are calculated from the financial statements of banks. Clustering validity is examined with the help of Silhouette values and graphs. As a result, it was determined that the sector formed three clusters in 2014. In 2015-2017 periods, cluster structures were not changed and banks were collected in two clusters. The technique has the advantage of separating the data set into clusters without the need for user information.

Suggested Citation

  • Mehmet OZCALICI, 2017. "Market Segmentation with Self-Organizing Maps in Banking Industry," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 11(2), pages 9-30.
    • Handle: RePEc:bdd:journl:v:11:y:2017:i:2:p:9-30
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    More about this item

    Keywords

    Artificial Neural Networks; Self-organizing Maps; Clustering; Segmentation;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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