Abstract :

In banking industry, optimum location of the bank branch plays an important role in ensuring the success of the bank.  It is one of the most important decision making processes.  This issue is highly important because of the vibrant competition, limited budgets and high customer expectations .This study’s objective is to provide a hybrid model for selecting optimal site location using available data sources and well accepted decision models, specifically Analytic Network Process (ANP) and Geographic Information System (GIS).The process identified the most commonly used criteria for bank branch location consideration through literature  review  yielding  demographics,  competition,  transportation,  access  to  public  facilities. Criteria and  sub-criteria  weights  were  quantified  through  pair-wise  comparison  using  expert  judges,  via  ANP.  The database created for the study area (Khartoum Locality) includes data about demographics, competition, transportation, access to public facilities available in the area.  With the help of Esri’s ArcGIS software through using the weighted overlay analysis tool we have identified suitable sites of new bank branches  .The results showed five optimal locations for new bank branch in Khartoum locality; location near Madani St, location close to Bashir Elnefedi St, location at  Firdous East Square 8, and two other sites in Khartoum West (Shajra Avenue).

For the conclusion, these results show the efficiency and applicability of the proposed integrated method.

---

Keywords :

Bank Branch Location, GIS, Multi criteria Decision Analysis, Site Selection Criteria.

---

References :

1. Miliotis, M. Dimopoulou, and I. Giannikos, “A Hierarchical Location Model for Locating Bank Branches in a Competitive Environment,” Int. Trans. Oper. Res., vol. 9, no. 5, pp. 549–565, 2002.
2. Allahi, M. Mobin, A. Vafadarnikjoo, and C. Salmon, “An Integrated AHP-GIS-MCLP Method to Locate Bank Branches,” Ind. Syst. Eng. Res. Conf. , 2015.
3. P. Gilje, E. Loutskina, and P. E. Strahan, “Exporting Liquidity: Branch Banking and Financial Integration,” J. Finance, vol. 71, no. 3, pp. 1159–1184, 2016.
4. H. Ata Can Bertay, Asli Demirgüç-Kunt, “Do we need big banks? Evidence on performance, strategy and market discipline,” J. Financ. Intermediation, vol. 22, no. 4, pp. 532–558, 2013.
5. T. Shehzad, J. de Haan, and B. Scholtens, “The impact of bank ownership concentration on impaired loans and capital adequacy,” J. Bank. Financ., vol. 34, no. 2, pp. 399–408, 2010.
6. Jain, V. Mahajan, and E. Muller, “Innovation Diffusion in the Presence of Supply Restrictions,” Mark. Sci., vol. 10, no. 1, pp. 83–90, 1991.
7. Shukrie, S. Anuar, and A. Alias, “Determination of New Bank Branch Location Using GIS Approach,” no. Rcstss 2014, pp. 147–156, 2016.
8. Tavakkoli-Moghaddam, S. M. Mousavi, and M. Heydar, “An integrated AHP-VIKOR methodology for plant location selection,” Int. J. Eng. Trans. B Appl., vol. 24, no. 2, pp. 127–137, 2011.
9. W. L. Cheng, H. Li, and L. Yu, “A GIS approach to shopping mall location selection,” Build. Environ., vol. 42, no. 2, pp. 884–892, 2007.
10. K. Bengar and J. Afshari, “Review and Site Selection for Mehre Eghtesad Banks ’ Branches Using Fuzzy AHP and Fuzzy TOPSIS,” vol. 11, no. 10, pp. 7159–7165, 2016.
11. V. Boufounou, “Evaluating bank branch location and performance: A case study,” Eur. J. Oper. Res., vol. 87, no. 2, pp. 389–402, 2014.
12. Cinar, “A Decision Support Model for Bank Branch Location Selection,” Int. J. Hum. Soc. Sci., vol. Vol.5, no. c, p. hal.846-851. Dhaka, Bangladesh, 2010.
13. Min, “A model-based decision support system for locating banks,” Inf. Manag., vol. 17, no. 4, pp. 207–215, 2012.
14. Y. Abbasi, “A decision support system for bank location selection,” vol. 16, no. 4, pp. 202–210, 2003.
15. Zhao and B. J. Garner, “Developing GIS Tools to Integrate MCDM Models for the Analysis of Bank Branch Closures,” 2015.