Location Based Mapping for ATMs through Geographic Information System (GIS)
Location-based Services (LBS) are web-based services that utilize location data from GPS-enabled mobile devices to provide real-time information to a mobile user based on his/her location. LBS make use of the Geographic Information System (GIS) technology and Internet to track the location of the users. User-initiated LBS requires internet connection whichis used to determine the location provide the search results output. GIS Technology is a computer defined system that helps in capturing, storing, manipulating, analysing and managing all types of spatial or geographical data.
Choosing a site for a new branch or ATM requires in-depth knowledge and skill. In the past, this task may have been outsourced. Nowadays, banks are finding it very efficient to choose their next service location using GIS.
Using list of criteria, including demographics, household income, buying trends, etc. options can be narrowed down and easily viewed on a map. Using GIS, you are empowered to make the decision about where to locate your next branch or ATM with complete confidence.
To retain existing customers and acquire new ones, financial institutions continuously increase the location convenience of their branches and ATMs. One way to achieve this is by expanding their ATM and / or branch networks. This is usually done in two ways: building new facilities for branches and ATMs; or acquiring potential partner's locations.
The second option is preferable partly because it can give banks immediate access to more customers. However, the locations of a potential partner may not necessarily be a good geographic fit to the existing ATM network. Therefore, such partnerships should be evaluated carefully so that the "right" ATM partner is selected.
Location convenience is an important factor when customers select a financial institution. A customer may find a bank convenient if it has a branch or an Automated Teller Machine (ATM) near his / her residence or workplace, say within 1 hour. To stay competitive, banks usually attempt to increase convenience (be as close as possible to customers) by expanding their bank and / or ATM networks. Basically, such expansions could be done in at least two ways: by building branches / installing ATMs in new locations (so called organic growth); or by acquiring an existing (e.g. competitor, partner, etc.) network. The former option is likely to be expensive and time consuming, so many banks resort to acquiring or partnering with already established ATM and / or branch networks.
BANSEFI follows a certain scheme which consists of three programs and two channels. The programs are Pal, Prospera and SinHambre. The two channels operating under this are 'open' and 'close'. The 'open' channel allows the beneficiary to withdraw money from any branch while the 'closed' channel allows the user to withdraw from only one branch. BANSEFI has 478 branches in 32 states, serving 1,161,561customers for whom they want to open ATMs which are within one hour driving distance for any given beneficiary.
It is always necessary to study the usability of GIS in identification of suitable sites for establishing new ATMs in Mexico. Evaluation of potential ATM partners should be done according to some criteria. For example, in the case of merger and acquisition of banks, revenue, market share, cost, or profits could be used to select the best partner. In our case (i.e. ATM partnership opportunity evaluation), since location convenience is important to stop existing customers from switching to competitors, it is important to "cover" as many customers as possible with ATMs. The first step is to find the number of ATMs needed (and their locations) to cover the customers in a given urban area. The objective of 'location set covering problem' (LSCP) is to find the minimum number of facilities (and their location) needed to cover all demand.
GIS is instrumental when it comes to Location-Based Mapping. GIS helped in representing spatial data, processing spatial data (e.g. performing spatial queries and buffers to determine the demand), preparing the data for the spatial optimization model, validation (i.e. showing the solutions of the optimization model on the map to assure that the model is applied properly) and display purposes (i.e. mapping).