Soft Computing

, Volume 23, Issue 16, pp 7193–7205 | Cite as

A model towards global demographics: an application—a universal bank branch geolocator based on branch size

  • Julia García CabelloEmail author
Methodologies and Application


Branch size strongly depends on branch cash holdings. However, while any exhaustive study into branch cash holdings must include demographics around branches, there are major variations when defining demographics according to “local” parameters, as opposed to “internationally accepted” ones. This wide fluctuation in definitions makes cross-border comparisons very difficult. The present paper intends to overcome these difficulties by developing a global spatial model that uses cash holdings as a major determinant of branch size and where geographical concepts are replaced by “internationally accepted” notions. Specifically, the contributions of this paper are twofold: firstly, it presents a theoretical model (based on Markov and Gibbs random fields) to analyse the branch cash holdings from a global spatial standpoint. Secondly, it introduces a universal branch geolocator based around a decision model that redesigns the bank branch network according to branch size. Importantly, the model variables (including branch size as the main criterion) can be replaced/expanded as needed through the use of a highly versatile decision-making tool that can be applied to a wide range of contexts, even non-banking ones as long as they are influenced by demographics.


Universal geolocator Branch size Branch cash holdings Spatial stochastic processes Demographic parameters 

Mathematics Subject Classification

C61 C63 G17 G21 



Financial support from the Spanish Ministry of Science and Innovation “Regulación Financiera y Sector Bancario en Tiempos de Inestabilidad: Mecanismos de Prevención y Resolución de la Crisis” (ECO2014-59584-P), Junta de Andalucía “Excellence Groups” (P12.SEJ.2463) and Junta de Andalucía (SEJ340) is gratefully acknowledged.

Compliance with ethical standards

Conflict of interest

The author declares that she has no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by the author.


  1. Abbasi GY (2003) A decision support system for bank location selection. Int J Comput Appl Technol 16:202–210. CrossRefGoogle Scholar
  2. Allahi S, Mobin M, Vafadarnikjoo A, Salmon C (2015) An integrated AHP-GIS-MCLP method to locate bank branches. In: Proceedings of the 2015 industrial and systems engineering research conference. ISBN: 978-098376244-7Google Scholar
  3. André M, Mahy G, Lejeune P, Bogaert J (2014) Toward a synthesis of the concept and a definition of the zones in the urban-rural gradient. Biotechnol Agron Soc 18(1):61–74Google Scholar
  4. Boufounou PV (1995) Theory and methodology, evaluating bank branch location and performance: a case study. Eur J Oper Res 87:389–402. zbMATHCrossRefGoogle Scholar
  5. Cabrerizo FJ, Al-Hmouz R, Morfeq A, Balamash AS, Martínez MA, Herrera-Viedma E (2017) Soft consensus measures in group decision making using unbalanced fuzzy linguistic information. Soft Comput 21(11):3037–3050. zbMATHCrossRefGoogle Scholar
  6. Cerutti E, Dell’ Ariccia G, Martínez Pería MS (2007) How banks go abroad: branches or subsidiaries? J Bank Financ 31:1669–1692. CrossRefGoogle Scholar
  7. Cinar N (2009) A decision support model for bank branch location selection. World Acad Sci Eng Technol 60:126–131Google Scholar
  8. García Cabello J (2017) The future of branch cash holdings management is here: new Markov chains. Eur J Oper Res 259(2):789–799. MathSciNetzbMATHCrossRefGoogle Scholar
  9. García Cabello J, Lobillo J (2017) Sound branch cash management for less: a low-cost forecasting algorithm under uncertain demand. Omega Int J Manag Sci 70(C):118–134. CrossRefGoogle Scholar
  10. Ioannou G, Mavri M (2007) Performance-net: a decision support system for reconfiguring a bank’s branch network. Omega Int J Manag Sci 35:190–201. CrossRefGoogle Scholar
  11. Glykas M, Xirogiannis AG (2005) A soft knowledge modeling approach for geographically dispersed financial organizations. Soft Comput 9:579–593. CrossRefGoogle Scholar
  12. Li C-C, Dong Y, Herrera F, Herrera-Viedma E, Martínez L (2017) Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Inf Fusion 33(1):29–40. CrossRefGoogle Scholar
  13. Miliotis P, Dimopoulou M, Giannikos I (2002) A hierarchical location model for locating bank branches in a competitive environment. Int T Oper Res 9(5):549–65. zbMATHCrossRefGoogle Scholar
  14. Ruiz-Hernandez D, Delgado-Gomez D, Lopez-Pascual J (2015) Restructuring bank networks after mergers and acquisitions: a capacitated delocation model for closing and resizing branches. Comput Oper Res 62:316–324. MathSciNetzbMATHCrossRefGoogle Scholar
  15. Wainwright MJ, Jordan MI (2008) Graphical models, exponential families, and variational inference. Found Trends\(^{\textregistered }\) Mach Learn 1(1–2):1–305 ISBN: 978-1-60198-184-4Google Scholar
  16. Zainab L, Zahra NA, Mostafa K (2014) Locating the bank branches using a hybrid method. Tech J Eng Appl Sci 4(3):124–134. CrossRefGoogle Scholar
  17. Zhou B (2016) Applying the Clique Percolation Method to analyzing crossmarket branch banking network structure: the case of Illinois. Soc Netw Anal Min 6(11):1–14. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied Mathematics, Faculty of Economics and Business SciencesUniversity of GranadaGranadaSpain

Personalised recommendations