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Rethinking Branch Banking Network

  • Oscar Granados
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

In recent years, banking services increased their digital services. However, they still require physical attention to customers and there may not be a definite extinction of the branches. Which is the best way to optimize the branch banking networks in megacities? This document proposes an alternative of branch banking network optimization, which uses genetic algorithms from information on the multi-layers structure of mobility, transportation, crime, cellular coverage, traffic and construction licenses. The results obtained define those locations where it may be more appropriate to establish a branch, as well as the need to merge or close other branches.

Keywords

Banking City science Complexity Strategy 

References

  1. 1.
    Carlson, M., Mitchener, K.: Branch banking as a device for discipline. J. Polit. Econ. 117(2), 165–210 (2009)CrossRefGoogle Scholar
  2. 2.
    Berger, A.N., Demsetz, R.S., Strahan, P.E.: The consolidation of the financial services industry: causes, consequences, and implications for the future. J. Bank. Financ. 23, 135–194 (1999)CrossRefGoogle Scholar
  3. 3.
    Dick, A.: Nationwide branch banking and its impact on market structure, quality and bank performance. J. Bus. 79, 567–592 (2006)CrossRefGoogle Scholar
  4. 4.
    Miller, J.H.: A Crude Look at the Whole: The Science of Complex Systems in Business, Life, and Society. Basic Books, New York (2016)Google Scholar
  5. 5.
    Bettencourt, L., West, G.: A unified theory of urban living. Nature 467(21), 912–913 (2010)ADSCrossRefGoogle Scholar
  6. 6.
    Barabási, A.-L.: Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Basic Books, Boston (2002)Google Scholar
  7. 7.
    Watts, D.: Six Degrees. The Science of a Connected Age. Norton & Company, New York (2003)Google Scholar
  8. 8.
    Friedmann, J.: The World City Hypothesis. Dev. Change 17(1), 69–83 (1986)CrossRefGoogle Scholar
  9. 9.
    Berger, A., Humphrey, D.: Efficiency of financial institutions: international survey and directions for future research. Eur. J. Oper. Res. 98(2), 175–212 (1997)CrossRefGoogle Scholar
  10. 10.
    Berger, A., Hanweck, G., Humphrey, D.: Competitive viability in banking: scale, scope, and product mix economies. J. Monet. Econ. 20(3), 501–520 (1987)CrossRefGoogle Scholar
  11. 11.
    Berger, A., Humphrey, D.: Measurement and efficiency issues in commercial banking. In: Griliches, Z. (ed.) Output Measurement in the Service Sectors. University of Chicago Press, Chicago (1992)Google Scholar
  12. 12.
    Lozano, S.: Slacks-based inefficiency approach for general networks with bad outputs: an application to the banking sector. Omega 60, 73–84 (2016)CrossRefGoogle Scholar
  13. 13.
    Wanke, P., Maredza, A., Gupta, R.: Merger and acquisitions in South African banking: a network DEA model. Res. Int. Bus. Financ. 41, 362–376 (2017)CrossRefGoogle Scholar
  14. 14.
    Jayo, M., Diniz, E., Zambaldi, F., Christopoulos, T.: Groups of services delivered by Brazilian branchless banking and respective network integration models. Electron. Commer. Res. Appl. 11(5), 504–517 (2012)CrossRefGoogle Scholar
  15. 15.
    Hirtle, B.: The impact of network size on bank branch performance. J. Bank. Financ. 31, 3782–3805 (2007)CrossRefGoogle Scholar
  16. 16.
    Sherman, H.D., Gold, F.: Bank branch operating efficiency: evaluation with data envelopment analysis. J. Bank. Financ. 9(2), 297–315 (1985)CrossRefGoogle Scholar
  17. 17.
    Schaffnit, C., Rosen, D., Paradi, J.: Best practice analysis of bank branches: an application of DEA in a large Canadian bank. Eur. J. Oper. Res. 98(2), 269–289 (1997)CrossRefGoogle Scholar
  18. 18.
    Paradi, J.C., Zhu, H.: A survey on bank branch efficiency and performance research with data envelopment analysis. Omega 41(1), 61–79 (2013)CrossRefGoogle Scholar
  19. 19.
    Gónzalez, M., Hidalgo, C., Barabási, A.-L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)ADSCrossRefGoogle Scholar
  20. 20.
    Bettencourt, L., West, G.: Bigger cities do more with less. Sci. Am. 305(2), 52–53 (2011)CrossRefGoogle Scholar
  21. 21.
    Glaeser, E.: Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier. Penguin Books, New York (2012)Google Scholar
  22. 22.
    Johnson, N.: Simply Complexity. One World Publications, Oxford (2007)Google Scholar
  23. 23.
    Widhalm, P., Yang, Y., Ulm, M., Athavale, S., Gonzalez, M.C.: Discovering urban activity patterns in cell phone data. Transportation 42(4), 597–623 (2015)CrossRefGoogle Scholar
  24. 24.
    De Domenico, M., Lima, A., González, M., Arenas, A.: Personalized routing for multitudes in smart cities. EPJ Data Sci. 4(1), 2–11 (2015)CrossRefGoogle Scholar
  25. 25.
    Florez, M., Jiang, S., Li, R., Mojica, C., Rios, R., González, M. (2016). humnetlab.mit.edu/wordpress/wp-content/uploads/2016/03/bogotatrb_2017.pdf. Accessed Jan 2018
  26. 26.
    Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J., Moreno, Y., Porter, M.: Multilayer networks. J. Complex Netw. 2, 203–271 (2014)CrossRefGoogle Scholar
  27. 27.
    Davis, L.: Handbook of Genetic Algorithms. International Thompson Computer Press, Boston (1996)Google Scholar
  28. 28.
    Cortinhal, M.J., Captivo, M.E.: Genetic algorithms for the single source capacitated location problem. In: Metaheuristics: Computer Decision-Making. Applied Optimization, vol. 86, pp. 187–216. Springer, Boston (2003)Google Scholar
  29. 29.
    Kung, L.-C., Liao, W.-H.: An approximation algorithm for a competitive facility location problem with network effects. Eur. J. Oper. Res. 267(1), 176–186 (2018)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Jaramillo, J., Bhadury, J., Batta, R.: On the use of genetic algorithms to solve location problems. Comput. Oper. Res. 29(6), 761–779 (2002)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Cohen, J.: Human population: the next half-century. Science 302(5648), 1172–1175 (2003)ADSCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of EconomicsUniversidad Jorge Tadeo LozanoBogotáColombia

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