Recently when I heard that SunTrust and BB&T merged, I casually wondered about the footprint of the combined bank. Although the number of branches is not necessarily an indicator of the health or performance of a bank, there are benefits to customers of having brick and mortar branches of commercial banks. A few of these include:

• Readily available ATMs to help in cash withdrawals
• Customers’ ability to transact large cash withdrawals
• More robust banking relationships as customers tend to more readily trust banks with branches they can walk into
• Human contact to answer money-related questions

Quite often, a data scientist’s job is to summarize and communicate information in a clear and concise manner. We’ve all heard the phrase, “a map is worth a thousand words”, and with good reason. Maps are easy to interpret, they are nice to look at and they give us context without having to use too many words. In that same spirit, below we present some maps highlighting the geographic coverage of SunTrust, BB&T and that of the combined entities. A visual approach seems to be a rather pleasing way to allow an audience to rapidly understand the raw location data.

In the interest of full disclosure, I must admit I got this idea from a blog post by Michael Helbraun who analyzed the Marriott acquisition of Starwood properties. Like Mr. Helbraun’s conundrum, the information used to generate the maps is not readily accessible. Therefore, it was necessary to reach into my data science toolkit to write a script to ‘scrape’ data from a website containing bank branch location information and store it in a database. This is easily done using the RJSONIO package in R. The sample code below demonstrates how to obtain latitude and longitude information from the Google API. As an example, we use RJSONIO to obtain the latitude and longitude information for the location “1850 Route 33 Trenton, NJ 08690”.  

library(RJSONIO)
url = "http://maps.google.com/maps/api/geocode/json?address="
url = URLencode(paste(url, "1850 Route 33 Trenton, NJ 08690", "&sensor=false", sep = ""))
Geocode_location = fromJSON(url, simplify = FALSE)

Once the database of latitudes and longitudes was completed I had a choice of mapping packages (such as therthreejs or leaflet packages). I chose to use the ggmap package (resolution is done against the Google API) to generate the maps.  

library(ggmap) 
usPlot = qmap(location = 'USA', zoom = 4, legend = "topright", maptype = "terrain", color = "bw", darken = 0.01) 
usPlot = usPlot + geom_point(data = Geocode_location, aes(y = lat, x = long, colour = firm)) + scale_color_manual(values=c("#56B4E9" ))
(usPlot = usPlot + scale_size_continuous(range = c(1,1)))

The map below shows a plot of all of the SunTrust branches in the contiguous United States. SunTrust primarily operates within the Southeast and mid-Atlantic regions.

On the other hand, like SunTrust, BB&T operates within the Southeast and Mid-Atlantic, but also extends into communities in Texas.

In the visual below, it is fairly obvious that the combined footprint of the two banks is roughly the same as that of the individual banks, showing significant overlap in certain areas.

In a straightforward scenario, the merger of SunTrust and BB&T should result in a combined bank with asset size to rival that of U.S. Bancorp, another domestic financial entity that we use for comparison.

The map below displays the expansive area covered by U.S. Bancorp.

It appears that the SunTrust-BB&T merger did not gain new terrain in terms of footprint as they already exist in the same population centers. One wonders about a different merger, say, between SunTrust and KeyBank (whose combined hypothetical footprint is shown below)…

… or one between BB&T and KeyBank (shown below), which seems to make more sense. KeyBank’s footprint covers both coasts in the Northern U.S. states, with a few Western territories.

So, why would the Boards of these two banks agree to a merger? Of course, in reality, geographic presence is not the only (or even the most important) consideration when these ventures are undertaken.

Other aspects reveal a myriad of priorities and focal points for executive management:

1. Regulatory requirements introduced in the wake of the financial crisis have driven up the cost of business, and merging is one way to reduce those costs. 
2. Other larger regional bank mergers, in turn put pressure on the rest of the industry.
3. ‘If banks don’t come up with ways to innovate, they die.’ Innovation in the form of acquired businesses can improve the stability of the resulting entities.
4. One of the biggest drivers behind bank mergers is having more money to allocate to technological improvement and other efficiency developments.

In summary, many factors influence mergers and acquisitions. However, our contemporaneous illustration demonstrates how useful a little data science “magic” can turn out to be for casual observers, with relatively marginal effort.

Happy visualization!