Visual Exploration and Analysis of Bank Performance Using Self Organizing Map

  • Mouna KessentiniEmail author
  • Esther JeffersEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 146)


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


Information visualization Self-Organizing map Performance analysis Banking 


  1. 1.
    Tufte, E.R.: Visual Explanations: Images and Quantities, Evidence and Narrative, 1st ed. Graphics Press, Pristine Condition, Cheshire (1997)Google Scholar
  2. 2.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, New York (1999)Google Scholar
  3. 3.
    Perin, C.: Direct manipulation for information visualization. Thèse de doctorat Université Paris Sud - Paris XI (2014)Google Scholar
  4. 4.
    Ware, C., Bobrow, R.: Motion to support rapid interactive queries on node-link diagrams. ACM Trans. Appl. Percept. 1(1), 3–18 (2004)CrossRefGoogle Scholar
  5. 5.
    Peng, W., Ward, M.O., Rundensteiner, E.A.: Clustter reduction in multi-dimensional data visualization using dimension reordering. In: Proceedings of the IEEE Symposium on Information Visualization (infovis’04), pp. 89–96. IEEE, Austin, TX, USA (2004)Google Scholar
  6. 6.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Golany, B., Storbeck, J.: A data envelopment analysis of the operational efficiency of bank branches. Interfaces 29(3), 14–26 (1999)CrossRefGoogle Scholar
  8. 8.
    Mostafa, M.: Clustering the ecological footprint of nations using Kohonen’s self organizing laps. Expert Syst. Appl. 37(4), 2747–2755 (2010)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self organized formation of topological correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195(1), 215–243 (1968)CrossRefGoogle Scholar
  11. 11.
    Gray, R.M.: Vector quantization. IEEE ASSP Mag. 1(2), 4–29 (1984)CrossRefGoogle Scholar
  12. 12.
    Kaski, S., Lagus, K.: Comparing self-organizing maps. In: von der Malsburg,C., Sendho, B. (eds.) Lecture Notes in Computer Science, ser. 1112, pp. 809–814. Springer-Verlag, Berlin, Germany (1996)CrossRefGoogle Scholar
  13. 13.
    Najand, S., Lo, Z., Bavarian, B.: Application of self organizing neural networks for mobile robot environment learning. Neural Netw. Robot. 202(1), 85–96 (1993)CrossRefGoogle Scholar
  14. 14.
    Jagric, T., Bojnec, S., Jagric, V.: Optimized spiral spherical self-organizing map approach to sector analysis—the case of banking. Expert Syst. Appl. 42(13), 5531–5540 (2015)CrossRefGoogle Scholar
  15. 15.
    Mart’n del Br’o, B., Serrano-Cinca, C.: Self-organizing neural networks for the analysis and representation of data: some financial cases. Neural Comput. Appl., Springer Verlag, 1(3), 93–206 (1993)Google Scholar
  16. 16.
    Kiviluoto, K., Bergius, P.: Analyzing financial statements with the self organizing map. In: Proceeding WSOM’ 97 Workshop Self-Organizing Maps, 362–367, Helsinki University of Technology, Espoo, Finland (1997)Google Scholar
  17. 17.
    Sarlin, P.: Decomposing the global financial crisis: a self-organizing time map. Pattern Recogn. Lett. 34, 1701–1709 (2013)CrossRefGoogle Scholar
  18. 18.
    Sarlin, P., Zhiyuan, Y.: Clustering of the self-organizing time map. Neurocomputing 121, 317–327 (2013)CrossRefGoogle Scholar
  19. 19.
    Eklund, B., Back, H., Vanharanta, H., Visa, A.: Assessing the feasibility of self-organizing maps for data mining financial information. In: Proceedings of the Xth European Conference on Information Systems (ECIS 2002), pp. 528–537, Gdansk, Poland (2002)Google Scholar
  20. 20.
    Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self organizing map. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000)Google Scholar
  21. 21.
    Länsiluoto, A., Eklund, T., Back, B., Vanharanta, H., Visa, A.: Industry-specific cycles and companies’ financial performance comparison using self-organizing maps. Benchmarking: Int. J. 11, 267–286 (2004)CrossRefGoogle Scholar
  22. 22.
    Beck, T., Kunt, A., Merrouche, O.: Islamic vs. conventional banking: business model, efficiency and stability. J. Bank. Financ. 7, 433–447 (2013)CrossRefGoogle Scholar
  23. 23.
    Fethi, M., Pasiouras, F.: Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey. Eur. J. Oper. Res. 204, 189–198 (2010)zbMATHCrossRefGoogle Scholar
  24. 24.
    Olson, D., Zoubi, T.: Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region. Int. J. Account. 43, 45–65 (2008)CrossRefGoogle Scholar
  25. 25.
    Olson, D., Zoubi, T.: Convergence in bank performance for commercial and Islamic banks during and after the global financial crisis. Q. Rev. Econ. Financ. 65, 71–87 (2016)CrossRefGoogle Scholar
  26. 26.
    Abedifar, P., Molyneux, P., Tarazi, A.: Risk in Islamic banking. Rev. Financ. 17(6), 2035–2096 (2013)CrossRefGoogle Scholar
  27. 27.
    Boyd, J.H., Graham, S.L.: Risk, regulation, and bank holding company expansion into nonbanking. Q. Rev., Federal Reserve Bank of Minneapolis 10(2), 2–17 (1986)Google Scholar
  28. 28.
    Tan, Y.: The impacts of risk and competition on bank profitability in China. J. Int. Financ. Mark., Inst. Money 40, 85–110 (2016)CrossRefGoogle Scholar
  29. 29.
    Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013)CrossRefGoogle Scholar
  30. 30.
    Pal, N., Bezdek, J., Tsao, E.K.: Generalized clustering networks and Kohonen self-organizing scheme. IEEE Trans. Neural Netw. 4, 549–557 (1993)CrossRefGoogle Scholar
  31. 31.
    Zhao, W.L., Deng, C.H., Ngo, C.W.: k-means: a revisit. Neurocomputing 291, 195–206 (2018)CrossRefGoogle Scholar
  32. 32.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinbach, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Research Center on Industry, The Institutions and The Economical Systems of Amiens (CRIISEA)University of Picardie Jules VerneAmiens CédexFrance

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