Visual Exploration and Analysis of Bank Performance Using Self Organizing Map

M. Kessentini () and Esther Jeffers ()
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Esther Jeffers: CRIISEA - Centre de Recherche sur les Institutions, l'Industrie et les Systèmes Économiques d'Amiens - UR UPJV 3908 - UPJV - Université de Picardie Jules Verne

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Abstract: The visualization of high dimensional data has an important role to play as an artifact supporting exploratory data analysis. There is growing evidence of the effectiveness of information visualization as it provides help in understanding data, increases the level of cognitive functioning and performs pattern recognition. This paper deals with the usefulness of Self-Organizing Map (SOM) neural network in the area of the banking sector. We want to show how SOM can be useful to convert huge amounts of financial data into valuable information used to speed up the decision-making process and facilitate data analysis for deeper understanding \textcopyright 2020, Springer Nature Switzerland AG.

Date: 2020
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Published in Smart Innovation, Systems and Technologies, 146, Springer, pp.420--434, 2020, ⟨10.1007/978-3-030-21005-2_41⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03678293

DOI: 10.1007/978-3-030-21005-2_41

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